Machine Learning Guide
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Machine Learning/Deep Learning Frameworks.
Learning Resources for ML
ML Frameworks, Libraries, and Tools
Algorithms
PyTorch Development
TensorFlow Development
Core ML Development
Deep Learning Development
Reinforcement Learning Development
Computer Vision Development
Natural Language Processing (NLP) Development
Bioinformatics
CUDA Development
MATLAB Development
C/C++ Development
Java Development
Python Development
Scala Development
R Development
Julia Development
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Machine Learning is a branch of artificial intelligence (AI) focused on building apps using algorithms that learn from data models and improve their accuracy over time without needing to be programmed.
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Natural Language Processing (NLP) Best Practices by Microsoft
The Autonomous Driving Cookbook by Microsoft
Azure Machine Learning - ML as a Service | Microsoft Azure
How to run Jupyter Notebooks in your Azure Machine Learning workspace
Machine Learning and Artificial Intelligence | Amazon Web Services
Scheduling Jupyter notebooks on Amazon SageMaker ephemeral instances
AI & Machine Learning | Google Cloud
Using Jupyter Notebooks with Apache Spark on Google Cloud
Machine Learning | Apple Developer
Artificial Intelligence & Autopilot | Tesla
Meta AI Tools | Facebook
PyTorch Tutorials
TensorFlow Tutorials
JupyterLab
Stable Diffusion with Core ML on Apple Silicon
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Machine Learning by Stanford University by Andrew Ng | Coursera
AWS Training and Certification for Machine Learning (ML) Courses
Machine Learning Scholarship Program for Microsoft Azure | Udacity
Microsoft Certified: Azure Data Scientist Associate
Microsoft Certified: Azure AI Engineer Associate
Azure Machine Learning training and deployment
Learning Machine learning and artificial intelligence from Google Cloud Training
Machine Learning Crash Course for Google Cloud
Machine Learning Courses Online | Udemy
Machine Learning Courses Online | Coursera
Learn Machine Learning with Online Courses and Classes | edX
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Introduction To Machine Learning (PDF)
Artificial Intelligence: A Modern Approach by Stuart J. Russel and Peter Norvig
Deep Learning by Ian Goodfellow, Yoshoua Bengio, and Aaron Courville
The Hundred-Page Machine Learning Book by Andriy Burkov
Machine Learning by Tom M. Mitchell
Programming Collective Intelligence: Building Smart Web 2.0 Applications by Toby Segaran
Machine Learning: An Algorithmic Perspective, Second Edition
Pattern Recognition and Machine Learning by Christopher M. Bishop
Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper
Python Machine Learning: A Technical Approach to Machine Learning for Beginners by Leonard Eddison
Bayesian Reasoning and Machine Learning by David Barber
Machine Learning for Absolute Beginners: A Plain English Introduction by Oliver Theobald
Machine Learning in Action by Ben Wilson
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron
Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller & Sarah Guido
Machine Learning for Hackers: Case Studies and Algorithms to Get you Started by Drew Conway and John Myles White
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Distributed Machine Learning Patterns - Book (free to read online) + Code
Real World Machine Learning [Free Chapters]
An Introduction To Statistical Learning - Book + R Code
Elements of Statistical Learning - Book
Think Bayes - Book + Python Code
Mining Massive Datasets
A First Encounter with Machine Learning
Introduction to Machine Learning - Alex Smola and S.V.N. Vishwanathan
A Probabilistic Theory of Pattern Recognition
Introduction to Information Retrieval
Forecasting: principles and practice
Introduction to Machine Learning - Amnon Shashua
Reinforcement Learning
Machine Learning
A Quest for AI
R Programming for Data Science
Data Mining - Practical Machine Learning Tools and Techniques
Machine Learning with TensorFlow
Machine Learning Systems
Foundations of Machine Learning - Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
AI-Powered Search - Trey Grainger, Doug Turnbull, Max Irwin -
Ensemble Methods for Machine Learning - Gautam Kunapuli
Machine Learning Engineering in Action - Ben Wilson
Privacy-Preserving Machine Learning - J. Morris Chang, Di Zhuang, G. Dumindu Samaraweera
Automated Machine Learning in Action - Qingquan Song, Haifeng Jin, and Xia Hu
Distributed Machine Learning Patterns - Yuan Tang
Managing Machine Learning Projects: From design to deployment - Simon Thompson
Causal Machine Learning - Robert Ness
Bayesian Optimization in Action - Quan Nguyen
Machine Learning Algorithms in Depth) - Vadim Smolyakov
Optimization Algorithms - Alaa Khamis
Practical Gradient Boosting by Guillaume Saupin
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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Apple CoreML is a framework that helps integrate machine learning models into your app. Core ML provides a unified representation for all models. Your app uses Core ML APIs and user data to make predictions, and to train or fine-tune models, all on the user's device. A model is the result of applying a machine learning algorithm to a set of training data. You use a model to make predictions based on new input data.
Apache OpenNLP is an open-source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like Named Entity Recognition, Sentence Detection, POS(Part-Of-Speech) tagging, Tokenization Feature extraction, Chunking, Parsing, and Coreference resolution.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
AutoGluon is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Weka is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
nGraph is an open source C++ library, compiler and runtime for Deep Learning. The nGraph Compiler aims to accelerate developing AI workloads using any deep learning framework and deploying to a variety of hardware targets.It provides the freedom, performance, and ease-of-use to AI developers.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
Huginn is a self-hosted system for building agents that perform automated tasks for you online. It can read the web, watch for events, and take actions on your behalf. Huginn's Agents create and consume events, propagating them along a directed graph. Think of it as a hackable version of IFTTT or Zapier on your own server.
Netron is a viewer for neural network, deep learning and machine learning models. It supports ONNX, TensorFlow Lite, Caffe, Keras, Darknet, PaddlePaddle, ncnn, MNN, Core ML, RKNN, MXNet, MindSpore Lite, TNN, Barracuda, Tengine, CNTK, TensorFlow.js, Caffe2 and UFF.
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
DALI is a GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
MindSpore Lite is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
PaddlePaddle is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
GoogleNotebookLM is an experimental AI tool using the power of language models paired with your existing content to gain critical insights, faster. Similar to a virtual research assistant that can summarize facts, explain complex ideas, and brainstorm new connections based on the sources you select.
Unilm is a large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities.
Semantic Kernel (SK) is a lightweight SDK enabling integration of AI Large Language Models (LLMs) with conventional programming languages. The SK extensible programming model combines natural language semantic functions, traditional code native functions, and embeddings-based memory unlocking new potential and adding value to applications with AI.
Pandas AI is a Python library that integrates generative artificial intelligence capabilities into Pandas, making dataframes conversational.
NCNN is a high-performance neural network inference framework optimized for the mobile platform.
MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba.
MediaPipe is an optimized for end-to-end performance on a wide array of platforms. See demos Learn more Complex on-device ML, simplified We've abstracted away the complexities of making on-device ML customizable, production-ready, and accessible across platforms.
MegEngine is a fast, scalable, and user friendly deep learning framework with 3 key features: Unified framework for both training and inference.
ML.NET is a machine learning library that is designed as an extensible platform so that you can consume other popular ML frameworks (TensorFlow, ONNX, Infer.NET, and more) and have access to even more machine learning scenarios, like image classification, object detection, and more.
Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system.
MMdnn is a comprehensive and cross-framework tool to convert, visualize and diagnose deep learning (DL) models. The "MM" stands for model management, and "dnn" is the acronym of deep neural network. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets.
GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models, based on PyTorch and MXNet.
MindsDB is a ML-SQL Server enables machine learning workflows for the most powerful databases and data warehouses using SQL.
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Tensorman is a utility for easy management of Tensorflow containers by developed by System76.Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.
Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.
Emu is a GPGPU library for Rust with a focus on portability, modularity, and performance. It's a CUDA-esque compute-specific abstraction over WebGPU providing specific functionality to make WebGPU feel more like CUDA.
Scalene is a high-performance CPU, GPU and memory profiler for Python that does a number of things that other Python profilers do not and cannot do. It runs orders of magnitude faster than many other profilers while delivering far more detailed information.
MLpack is a fast, flexible C++ machine learning library written in C++ and built on the Armadillo linear algebra library, the ensmallen numerical optimization library, and parts of Boost.
Netron is a viewer for neural network, deep learning and machine learning models. It supports ONNX, TensorFlow Lite, Caffe, Keras, Darknet, PaddlePaddle, ncnn, MNN, Core ML, RKNN, MXNet, MindSpore Lite, TNN, Barracuda, Tengine, CNTK, TensorFlow.js, Caffe2 and UFF.
Lightning is a tool that builds and trains PyTorch models and connect them to the ML lifecycle using Lightning App templates, without handling DIY infrastructure, cost management, scaling, etc..
OpenNN is an open-source neural networks library for machine learning. It contains sophisticated algorithms and utilities to deal with many artificial intelligence solutions.
H20 is an AI Cloud platform that solves complex business problems and accelerates the discovery of new ideas with results you can understand and trust.
Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
llama.cpp is a Port of Facebook's LLaMA model in C/C++.
hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models.
Nextjournal is a notebook for reproducible research. It runs anything you can put into a Docker container. Improve your workflow with polyglot notebooks, automatic versioning and real-time collaboration. Save time and money with on-demand provisioning, including GPU support.
IPython provides a rich architecture for interactive computing with:
Veles is a Distributed platform for rapid Deep learning application development currently devloped by Samsung.
DyNet is a neural network library developed by Carnegie Mellon University and many others. It is written in C++ (with bindings in Python) and is designed to be efficient when run on either CPU or GPU, and to work well with networks that have dynamic structures that change for every training instance. These kinds of networks are particularly important in natural language processing tasks, and DyNet has been used to build state-of-the-art systems for syntactic parsing, machine translation, morphological inflection, and many other application areas.
Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a toolkit of libraries (Ray AIR) for accelerating ML workloads.
whisper.cpp is a high-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model.
ChatGPT Plus is a pilot subscription plan($20/month) for ChatGPT, a conversational AI that can chat with you, answer follow-up questions, and challenge incorrect assumptions.
Auto-GPT is an "AI agent" that given a goal in natural language, can attempt to achieve it by breaking it into sub-tasks and using the internet and other tools in an automatic loop. It uses OpenAI's GPT-4 or GPT-3.5 APIs, and is among the first examples of an application using GPT-4 to perform autonomous tasks.
Chatbot UI by mckaywrigley is an advanced chatbot kit for OpenAI's chat models built on top of Chatbot UI Lite using Next.js, TypeScript, and Tailwind CSS. This version of ChatBot UI supports both GPT-3.5 and GPT-4 models. Conversations are stored locally within your browser. You can export and import conversations to safeguard against data loss. See a demo.
Chatbot UI Lite by mckaywrigley is a simple chatbot starter kit for OpenAI's chat model using Next.js, TypeScript, and Tailwind CSS. See a demo.
MiniGPT-4 is an enhancing Vision-language Understanding with Advanced Large Language Models.
GPT4All is an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue based on LLaMa.
GPT4All UI is a Flask web application that provides a chat UI for interacting with the GPT4All chatbot.
Alpaca.cpp is a fast ChatGPT-like model locally on your device. It combines the LLaMA foundation model with an open reproduction of Stanford Alpaca a fine-tuning of the base model to obey instructions (akin to the RLHF used to train ChatGPT) and a set of modifications to llama.cpp to add a chat interface.
llama.cpp is a Port of Facebook's LLaMA model in C/C++.
OpenPlayground is a playfround for running ChatGPT-like models locally on your device.
Vicuna is an open source chatbot trained by fine tuning LLaMA. It apparently achieves more than 90% quality of chatgpt and costs $300 to train.
Yeagar ai is a Langchain Agent creator designed to help you build, prototype, and deploy AI-powered agents with ease.
Vicuna is created by fine-tuning a LLaMA base model using approximately 70K user-shared conversations gathered from ShareGPT.com with public APIs. To ensure data quality, it convert the HTML back to markdown and filter out some inappropriate or low-quality samples.
ShareGPT is a place to share your wildest ChatGPT conversations with one click. With 198,404 conversations shared so far.
FastChat is an open platform for training, serving, and evaluating large language model based chatbots.
Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs (GPT-4, ChatGPT and alike). It offers production-ready tools to quickly build complex decision making, question answering, semantic search, text generation applications, and more.
StableLM (Stability AI Language Models) is StableLM series of language models and will be continuously updated with new checkpoints.
Databricks’ Dolly is an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use.
GPTCach is a Library for Creating Semantic Cache for LLM Queries.
AlaC is an Artificial Intelligence Infrastructure-as-Code Generator.
Adrenaline is a tool that lets you talk to your codebase. It's powered by static analysis, vector search, and large language models.
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
DoctorGPT is a lightweight self-contained binary that monitors your application logs for problems and diagnoses them.
HttpGPT is an Unreal Engine 5 plugin that facilitates integration with OpenAI's GPT based services (ChatGPT and DALL-E) through asynchronous REST requests, making it easy for developers to communicate with these services. It also includes Editor Tools to integrate Chat GPT and DALL-E image generation directly in the Engine.
PaLM 2 is a next generation large language model that builds on Google’s legacy of breakthrough research in machine learning and responsible AI. It includes an advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than our previous state-of-the-art LLMs.
Med-PaLM is a large language model (LLM) designed to provide high quality answers to medical questions. It harnesses the power of Google’s large language models, which we have aligned to the medical domain with a set of carefully-curated medical expert demonstrations.
Sec-PaLM is a large language models (LLMs), that accelerate the ability to help people who are responsible for keeping their organizations safe. These new models not only give people a more natural and creative way to understand and manage security.
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LocalAI is a self-hosted, community-driven, local OpenAI-compatible API. Drop-in replacement for OpenAI running LLMs on consumer-grade hardware with no GPU required. It's an API to run ggml compatible models: llama, gpt4all, rwkv, whisper, vicuna, koala, gpt4all-j, cerebras, falcon, dolly, starcoder, and many others.
llama.cpp is a Port of Facebook's LLaMA model in C/C++.
ollama is a tool to get up and running with Llama 2 and other large language models locally.
LocalAI is a self-hosted, community-driven, local OpenAI-compatible API. Drop-in replacement for OpenAI running LLMs on consumer-grade hardware with no GPU required. It's an API to run ggml compatible models: llama, gpt4all, rwkv, whisper, vicuna, koala, gpt4all-j, cerebras, falcon, dolly, starcoder, and many others.
Serge is a web interface for chatting with Alpaca through llama.cpp. Fully self-hosted & dockerized, with an easy to use API.
OpenLLM is an open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease.
Llama-gpt is a self-hosted, offline, ChatGPT-like chatbot. Powered by Llama 2. 100% private, with no data leaving your device.
Llama2 webui is a tool to run any Llama 2 locally with gradio UI on GPU or CPU from anywhere (Linux/Windows/Mac). Use llama2-wrapper as your local llama2 backend for Generative Agents/Apps.
Llama2.c is a tool to Train the Llama 2 LLM architecture in PyTorch then inference it with one simple 700-line C file (run.c).
Alpaca.cpp is a fast ChatGPT-like model locally on your device. It combines the LLaMA foundation model with an open reproduction of Stanford Alpaca a fine-tuning of the base model to obey instructions (akin to the RLHF used to train ChatGPT) and a set of modifications to llama.cpp to add a chat interface.
GPT4All is an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue based on LLaMa.
MiniGPT-4 is an enhancing Vision-language Understanding with Advanced Large Language Models
LoLLMS WebUI is a the hub for LLM (Large Language Model) models. It aims to provide a user-friendly interface to access and utilize various LLM models for a wide range of tasks. Whether you need help with writing, coding, organizing data, generating images, or seeking answers to your questions.
LM Studio is a tool to Discover, download, and run local LLMs.
Gradio Web UI is a tool for Large Language Models. Supports transformers, GPTQ, llama.cpp (ggml/gguf), Llama models.
OpenPlayground is a playfround for running ChatGPT-like models locally on your device.
Vicuna is an open source chatbot trained by fine tuning LLaMA. It apparently achieves more than 90% quality of chatgpt and costs $300 to train.
Yeagar ai is a Langchain Agent creator designed to help you build, prototype, and deploy AI-powered agents with ease.
KoboldCpp is an easy-to-use AI text-generation software for GGML models. It's a single self contained distributable from Concedo, that builds off llama.cpp, and adds a versatile Kobold API endpoint, additional format support, backward compatibility, as well as a fancy UI with persistent stories, editing tools, save formats, memory, world info, author's note, characters, and scenarios.
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Fuzzy logic is a heuristic approach that allows for more advanced decision-tree processing and better integration with rules-based programming.
Architecture of a Fuzzy Logic System. Source: ResearchGate
Support Vector Machine (SVM) is a supervised machine learning model that uses classification algorithms for two-group classification problems.
Support Vector Machine (SVM). Source:OpenClipArt
Neural networks are a subset of machine learning and are at the heart of deep learning algorithms. The name/structure is inspired by the human brain copying the process that biological neurons/nodes signal to one another.
Deep neural network. Source: IBM
Convolutional Neural Networks (R-CNN) is an object detection algorithm that first segments the image to find potential relevant bounding boxes and then run the detection algorithm to find most probable objects in those bounding boxes.
Convolutional Neural Networks. Source:CS231n
Recurrent neural networks (RNNs) is a type of artificial neural network which uses sequential data or time series data.
Recurrent Neural Networks. Source: Slideteam
Multilayer Perceptrons (MLPs) is multi-layer neural networks composed of multiple layers of perceptrons with a threshold activation.
Multilayer Perceptrons. Source: DeepAI
Random forest is a commonly-used machine learning algorithm, which combines the output of multiple decision trees to reach a single result. A decision tree in a forest cannot be pruned for sampling and therefore, prediction selection. Its ease of use and flexibility have fueled its adoption, as it handles both classification and regression problems.
Random forest. Source: wikimedia
Decision trees are tree-structured models for classification and regression.
**Decision Trees. Source: CMU
Naive Bayes is a machine learning algorithm that is used solved calssification problems. It's based on applying Bayes' theorem with strong independence assumptions between the features.
Bayes' theorem. Source:mathisfun
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PyTorch is an open-source deep learning framework that accelerates the path from research to production, used for applications such as computer vision and natural language processing. PyTorch is developed by Facebook's AI Research lab.
Getting Started with PyTorch
PyTorch Documentation
PyTorch Discussion Forum
Top Pytorch Courses Online | Coursera
Top Pytorch Courses Online | Udemy
Learn PyTorch with Online Courses and Classes | edX
PyTorch Fundamentals - Learn | Microsoft Docs
Intro to Deep Learning with PyTorch | Udacity
PyTorch Development in Visual Studio Code
PyTorch on Azure - Deep Learning with PyTorch | Microsoft Azure
PyTorch - Azure Databricks | Microsoft Docs
Deep Learning with PyTorch | Amazon Web Services (AWS)
Getting started with PyTorch on Google Cloud
PyTorch Mobile is an end-to-end ML workflow from Training to Deployment for iOS and Android mobile devices.
TorchScript is a way to create serializable and optimizable models from PyTorch code. This allows any TorchScript program to be saved from a Python process and loaded in a process where there is no Python dependency.
TorchServe is a flexible and easy to use tool for serving PyTorch models.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
ONNX Runtime is a cross-platform, high performance ML inferencing and training accelerator. It supports models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc.
Kornia is a differentiable computer vision library that consists of a set of routines and differentiable modules to solve generic CV (Computer Vision) problems.
PyTorch-NLP is a library for Natural Language Processing (NLP) in Python. It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders.
Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
Hummingbird is a library for compiling trained traditional ML models into tensor computations. It allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.
Deep Graph Library (DGL) is a Python package built for easy implementation of graph neural network model family, on top of PyTorch and other frameworks.
TensorLy is a high level API for tensor methods and deep tensorized neural networks in Python that aims to make tensor learning simple.
GPyTorch is a Gaussian process library implemented using PyTorch, designed for creating scalable, flexible Gaussian process models.
Poutyne is a Keras-like framework for PyTorch and handles much of the boilerplating code needed to train neural networks.
Forte is a toolkit for building NLP pipelines featuring composable components, convenient data interfaces, and cross-task interaction.
TorchMetrics is a Machine learning metrics for distributed, scalable PyTorch applications.
Captum is an open source, extensible library for model interpretability built on PyTorch.
Transformer is a State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
Hydra is a framework for elegantly configuring complex applications.
Accelerate is a simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.
Ray is a fast and simple framework for building and running distributed applications.
ParlAI is a unified platform for sharing, training, and evaluating dialog models across many tasks.
PyTorchVideo is a deep learning library for video understanding research. Hosts various video-focused models, datasets, training pipelines and more.
Opacus is a library that enables training PyTorch models with Differential Privacy.
PyTorch Lightning is a Keras-like ML library for PyTorch. It leaves core training and validation logic to you and automates the rest.
PyTorch Geometric Temporal is a temporal (dynamic) extension library for PyTorch Geometric.
PyTorch Geometric is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds.
Raster Vision is an open source framework for deep learning on satellite and aerial imagery.
CrypTen is a framework for Privacy Preserving ML. Its goal is to make secure computing techniques accessible to ML practitioners.
Optuna is an open source hyperparameter optimization framework to automate hyperparameter search.
Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend.
Albumentations is a fast and extensible image augmentation library for different CV tasks like classification, segmentation, object detection and pose estimation.
Skorch is a high-level library for PyTorch that provides full scikit-learn compatibility.
MMF is a modular framework for vision & language multimodal research from Facebook AI Research (FAIR).
AdaptDL is a resource-adaptive deep learning training and scheduling framework.
Polyaxon is a platform for building, training, and monitoring large-scale deep learning applications.
TextBrewer is a PyTorch-based knowledge distillation toolkit for natural language processing
AdverTorch is a toolbox for adversarial robustness research. It contains modules for generating adversarial examples and defending against attacks.
NeMo is a a toolkit for conversational AI.
ClinicaDL is a framework for reproducible classification of Alzheimer's Disease
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch.
TorchIO is a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch.
PySyft is a Python library for encrypted, privacy preserving deep learning.
Flair is a very simple framework for state-of-the-art natural language processing (NLP).
Glow is a ML compiler that accelerates the performance of deep learning frameworks on different hardware platforms.
FairScale is a PyTorch extension library for high performance and large scale training on one or multiple machines/nodes.
MONAI is a deep learning framework that provides domain-optimized foundational capabilities for developing healthcare imaging training workflows.
PFRL is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using PyTorch.
Einops is a flexible and powerful tensor operations for readable and reliable code.
PyTorch3D is a deep learning library that provides efficient, reusable components for 3D Computer Vision research with PyTorch.
Ensemble Pytorch is a unified ensemble framework for PyTorch to improve the performance and robustness of your deep learning model.
Lightly is a computer vision framework for self-supervised learning.
Higher is a library which facilitates the implementation of arbitrarily complex gradient-based meta-learning algorithms and nested optimisation loops with near-vanilla PyTorch.
Horovod is a distributed training library for deep learning frameworks. Horovod aims to make distributed DL fast and easy to use.
PennyLane is a library for quantum ML, automatic differentiation, and optimization of hybrid quantum-classical computations.
Detectron2 is FAIR's next-generation platform for object detection and segmentation.
Fastai is a library that simplifies training fast and accurate neural nets using modern best practices.
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TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Getting Started with TensorFlow
TensorFlow Tutorials
TensorFlow Developer Certificate | TensorFlow
TensorFlow Community
TensorFlow Models & Datasets
TensorFlow Cloud
Machine learning education | TensorFlow
Top Tensorflow Courses Online | Coursera
Top Tensorflow Courses Online | Udemy
Deep Learning with TensorFlow | Udemy
Deep Learning with Tensorflow | edX
Intro to TensorFlow for Deep Learning | Udacity
Intro to TensorFlow: Machine Learning Crash Course | Google Developers
Train and deploy a TensorFlow model - Azure Machine Learning
Apply machine learning models in Azure Functions with Python and TensorFlow | Microsoft Azure
Deep Learning with TensorFlow | Amazon Web Services (AWS)
TensorFlow - Amazon EMR | AWS Documentation
TensorFlow Enterprise | Google Cloud
TensorFlow Lite is an open source deep learning framework for deploying machine learning models on mobile and IoT devices.
TensorFlow.js is a JavaScript Library that lets you develop or execute ML models in JavaScript, and use ML directly in the browser client side, server side via Node.js, mobile native via React Native, desktop native via Electron, and even on IoT devices via Node.js on Raspberry Pi.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Google Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud, allowing you to execute TensorFlow code in your browser with a single click.
What-If Tool is a tool for code-free probing of machine learning models, useful for model understanding, debugging, and fairness. Available in TensorBoard and jupyter or colab notebooks.
TensorBoard is a suite of visualization tools to understand, debug, and optimize TensorFlow programs.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that optimizes TensorFlow computations. The results are improvements in speed, memory usage, and portability on server and mobile platforms.
ML Perf is a broad ML benchmark suite for measuring performance of ML software frameworks, ML hardware accelerators, and ML cloud platforms.
TensorFlow Playground is an development environment to tinker around with a neural network in your browser.
TPU Research Cloud (TRC) is a program enables researchers to apply for access to a cluster of more than 1,000 Cloud TPUs at no charge to help them accelerate the next wave of research breakthroughs.
MLIR is a new intermediate representation and compiler framework.
Lattice is a library for flexible, controlled and interpretable ML solutions with common-sense shape constraints.
TensorFlow Hub is a library for reusable machine learning. Download and reuse the latest trained models with a minimal amount of code.
TensorFlow Cloud is a library to connect your local environment to Google Cloud.
TensorFlow Model Optimization Toolkit is a suite of tools for optimizing ML models for deployment and execution.
TensorFlow Recommenders is a library for building recommender system models.
TensorFlow Text is a collection of text- and NLP-related classes and ops ready to use with TensorFlow 2.
TensorFlow Graphics is a library of computer graphics functionalities ranging from cameras, lights, and materials to renderers.
TensorFlow Federated is an open source framework for machine learning and other computations on decentralized data.
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis.
Tensor2Tensor is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
TensorFlow Privacy is a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform.
TensorFlow Agents is a library for reinforcement learning in TensorFlow.
TensorFlow Addons is a repository of contributions that conform to well-established API patterns, but implement new functionality not available in core TensorFlow, maintained by SIG Addons. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers.
TensorFlow I/O is a Dataset, streaming, and file system extensions, maintained by SIG IO.
TensorFlow Quantum is a quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models.
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
TRFL is a library for reinforcement learning building blocks created by DeepMind.
Mesh TensorFlow is a language for distributed deep learning, capable of specifying a broad class of distributed tensor computations.
RaggedTensors is an API that makes it easy to store and manipulate data with non-uniform shape, including text (words, sentences, characters), and batches of variable length.
Unicode Ops is an API that Supports working with Unicode text directly in TensorFlow.
Magenta is a research project exploring the role of machine learning in the process of creating art and music.
Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF.
Sonnet is a library from DeepMind for constructing neural networks.
Neural Structured Learning is a learning framework to train neural networks by leveraging structured signals in addition to feature inputs.
Model Remediation is a library to help create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.
Fairness Indicators is a library that enables easy computation of commonly-identified fairness metrics for binary and multiclass classifiers.
Decision Forests is a State-of-the-art algorithms for training, serving and interpreting models that use decision forests for classification, regression and ranking.
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Core ML is an Apple framework for integrating machine learning models into apps running on Apple devices (including iOS, watchOS, macOS, and tvOS). Core ML introduces a public file format (.mlmodel) for a broad set of ML methods including deep neural networks (both convolutional and recurrent), tree ensembles with boosting, and generalized linear models. Models in this format can be directly integrated into apps through Xcode.
Introduction to Core ML
Integrating a Core ML Model into your App
Core ML Models
Core ML API Reference
Core ML Specification
Apple Developer Forums for Core ML
Top Core ML Courses Online | Udemy
Top Core ML Courses Online | Coursera
IBM Watson Services for Core ML | IBM
Generate Core ML assets using IBM Maximo Visual Inspection | IBM
Core ML tools is a project that contains supporting tools for Core ML model conversion, editing, and validation.
Create ML is a tool that provides new ways of training machine learning models on your Mac. It takes the complexity out of model training while producing powerful Core ML models.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Apple Vision is a framework that performs face and face landmark detection, text detection, barcode recognition, image registration, and general feature tracking. Vision also allows the use of custom Core ML models for tasks like classification or object detection.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
Xcode includes everything developers need to create great applications for Mac, iPhone, iPad, Apple TV, and Apple Watch. Xcode provides developers a unified workflow for user interface design, coding, testing, and debugging. Xcode is built as an Universal app that runs 100% natively on Intel-based CPUs and Apple Silicon. It includes a unified macOS SDK that features all the frameworks, compilers, debuggers, and other tools you need to build apps that run natively on Apple Silicon and the Intel x86_64 CPU.
SwiftUI is a user interface toolkit that provides views, controls, and layout structures for declaring your app's user interface. The SwiftUI framework provides event handlers for delivering taps, gestures, and other types of input to your application.
UIKit is a framework provides the required infrastructure for your iOS or tvOS apps. It provides the window and view architecture for implementing your interface, the event handling infrastructure for delivering Multi-Touch and other types of input to your app, and the main run loop needed to manage interactions among the user, the system, and your app.
AppKit is a graphical user interface toolkit that contains all the objects you need to implement the user interface for a macOS app such as windows, panels, buttons, menus, scrollers, and text fields, and it handles all the details for you as it efficiently draws on the screen, communicates with hardware devices and screen buffers, clears areas of the screen before drawing, and clips views.
ARKit is a set set of software development tools to enable developers to build augmented-reality apps for iOS developed by Apple. The latest version ARKit 3.5 takes advantage of the new LiDAR Scanner and depth sensing system on iPad Pro(2020) to support a new generation of AR apps that use Scene Geometry for enhanced scene understanding and object occlusion.
RealityKit is a framework to implement high-performance 3D simulation and rendering with information provided by the ARKit framework to seamlessly integrate virtual objects into the real world.
SceneKit is a high-level 3D graphics framework that helps you create 3D animated scenes and effects in your iOS apps.
Instruments is a powerful and flexible performance-analysis and testing tool that’s part of the Xcode tool set. It’s designed to help you profile your iOS, watchOS, tvOS, and macOS apps, processes, and devices in order to better understand and optimize their behavior and performance.
Cocoapods is a dependency manager for Swift and Objective-C used in Xcode projects by specifying the dependencies for your project in a simple text file. CocoaPods then recursively resolves dependencies between libraries, fetches source code for all dependencies, and creates and maintains an Xcode workspace to build your project.
AppCode is constantly monitoring the quality of your code. It warns you of errors and smells and suggests quick-fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages.
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Deep Learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain,though, far from matching its ability. This allows the neural networks to "learn" from large amounts of data. The Learning can be supervised, semi-supervised or unsupervised.
Deep Learning Online Courses | NVIDIA
Top Deep Learning Courses Online | Coursera
Top Deep Learning Courses Online | Udemy
Learn Deep Learning with Online Courses and Lessons | edX
Deep Learning Online Course Nanodegree | Udacity
Machine Learning Course by Andrew Ng | Coursera
Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera
Data Science: Deep Learning and Neural Networks in Python | Udemy
Understanding Machine Learning with Python | Pluralsight
How to Think About Machine Learning Algorithms | Pluralsight
Deep Learning Courses | Stanford Online
Deep Learning - UW Professional & Continuing Education
Deep Learning Online Courses | Harvard University
Machine Learning for Everyone Courses | DataCamp
Artificial Intelligence Expert Course: Platinum Edition | Udemy
Top Artificial Intelligence Courses Online | Coursera
Learn Artificial Intelligence with Online Courses and Lessons | edX
Professional Certificate in Computer Science for Artificial Intelligence | edX
Artificial Intelligence Nanodegree program
Artificial Intelligence (AI) Online Courses | Udacity
Intro to Artificial Intelligence Course | Udacity
Edge AI for IoT Developers Course | Udacity
Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare
Expert Systems and Applied Artificial Intelligence
Autonomous Systems - Microsoft AI
Introduction to Microsoft Project Bonsai
Machine teaching with the Microsoft Autonomous Systems platform
Autonomous Maritime Systems Training | AMC Search
Top Autonomous Cars Courses Online | Udemy
Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy
Learn Autonomous Robotics with Online Courses and Lessons | edX
Artificial Intelligence Nanodegree program
Autonomous Systems Online Courses & Programs | Udacity
Edge AI for IoT Developers Course | Udacity
Autonomous Systems MOOC and Free Online Courses | MOOC List
Robotics and Autonomous Systems Graduate Program | Standford Online
Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
NVIDIA DLSS (Deep Learning Super Sampling) is a temporal image upscaling AI rendering technology that increases graphics performance using dedicated Tensor Core AI processors on GeForce RTX™ GPUs. DLSS uses the power of a deep learning neural network to boost frame rates and generate beautiful, sharp images for your games.
AMD FidelityFX Super Resolution (FSR) is an open source, high-quality solution for producing high resolution frames from lower resolution inputs. It uses a collection of cutting-edge Deep Learning algorithms with a particular emphasis on creating high-quality edges, giving large performance improvements compared to rendering at native resolution directly. FSR enables “practical performance” for costly render operations, such as hardware ray tracing for the AMD RDNA™ and AMD RDNA™ 2 architectures.
Intel Xe Super Sampling (XeSS) is a temporal image upscaling AI rendering technology that increases graphics performance similar to NVIDIA's DLSS (Deep Learning Super Sampling). Intel's Arc GPU architecture (early 2022) will have GPUs that feature dedicated Xe-cores to run XeSS. The GPUs will have Xe Matrix eXtenstions matrix (XMX) engines for hardware-accelerated AI processing. XeSS will be able to run on devices without XMX, including integrated graphics, though, the performance of XeSS will be lower on non-Intel graphics cards because it will be powered by DP4a instruction.
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
AutoGluon is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Weka is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
Microsoft Project Bonsai is a low-code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
Microsoft AirSim is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
ROS/ROS2 bridge for CARLA(package) is a bridge that enables two-way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Robotics Toolbox™ is a tool that provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Predictive Maintenance Toolbox™ is a tool that lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine. The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
Automated Driving Toolbox™ is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Navigation Toolbox™ is a tool that provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
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Reinforcement Learning is a subset of machine learning, which is a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain,though, far from matching its ability. This allows the neural networks to "learn" from a process in which a model learns to become more accurate for performing an action in an environment based on feedback in order to maximize the reward. The Learning can be supervised, semi-supervised or unsupervised.
Top Reinforcement Learning Courses | Coursera
Top Reinforcement Learning Courses | Udemy
Top Reinforcement Learning Courses | Udacity
Reinforcement Learning Courses | Stanford Online
Deep Learning Online Courses | NVIDIA
Top Deep Learning Courses Online | Coursera
Top Deep Learning Courses Online | Udemy
Learn Deep Learning with Online Courses and Lessons | edX
Deep Learning Online Course Nanodegree | Udacity
Machine Learning Course by Andrew Ng | Coursera
Machine Learning Engineering for Production (MLOps) course by Andrew Ng | Coursera
Data Science: Deep Learning and Neural Networks in Python | Udemy
Understanding Machine Learning with Python | Pluralsight
How to Think About Machine Learning Algorithms | Pluralsight
Deep Learning Courses | Stanford Online
Deep Learning - UW Professional & Continuing Education
Deep Learning Online Courses | Harvard University
Machine Learning for Everyone Courses | DataCamp
Artificial Intelligence Expert Course: Platinum Edition | Udemy
Top Artificial Intelligence Courses Online | Coursera
Learn Artificial Intelligence with Online Courses and Lessons | edX
Professional Certificate in Computer Science for Artificial Intelligence | edX
Artificial Intelligence Nanodegree program
Artificial Intelligence (AI) Online Courses | Udacity
Intro to Artificial Intelligence Course | Udacity
Edge AI for IoT Developers Course | Udacity
Reasoning: Goal Trees and Rule-Based Expert Systems | MIT OpenCourseWare
Expert Systems and Applied Artificial Intelligence
Autonomous Systems - Microsoft AI
Introduction to Microsoft Project Bonsai
Machine teaching with the Microsoft Autonomous Systems platform
Autonomous Maritime Systems Training | AMC Search
Top Autonomous Cars Courses Online | Udemy
Applied Control Systems 1: autonomous cars: Math + PID + MPC | Udemy
Learn Autonomous Robotics with Online Courses and Lessons | edX
Artificial Intelligence Nanodegree program
Autonomous Systems Online Courses & Programs | Udacity
Edge AI for IoT Developers Course | Udacity
Autonomous Systems MOOC and Free Online Courses | MOOC List
Robotics and Autonomous Systems Graduate Program | Standford Online
Mobile Autonomous Systems Laboratory | MIT OpenCourseWare
OpenAI is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API.
ReinforcementLearning.jl is a collection of tools for doing reinforcement learning research in Julia.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.
AWS RoboMaker is a service that provides a fully-managed, scalable infrastructure for simulation that customers use for multi-robot simulation and CI/CD integration with regression testing in simulation.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. It supports distributed training on multiple machines, including AWS, GCE, Azure, and Yarn clusters. Also, it can be integrated with Flink, Spark and other cloud dataflow systems.
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.
AutoGluon is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Weka is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
Microsoft Project Bonsai is a low-code AI platform that speeds AI-powered automation development and part of the Autonomous Systems suite from Microsoft. Bonsai is used to build AI components that can provide operator guidance or make independent decisions to optimize process variables, improve production efficiency, and reduce downtime.
Microsoft AirSim is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
CARLA is an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely.
ROS/ROS2 bridge for CARLA(package) is a bridge that enables two-way communication between ROS and CARLA. The information from the CARLA server is translated to ROS topics. In the same way, the messages sent between nodes in ROS get translated to commands to be applied in CARLA.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Robotics Toolbox™ is a tool that provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Predictive Maintenance Toolbox™ is a tool that lets you manage sensor data, design condition indicators, and estimate the remaining useful life (RUL) of a machine. The toolbox provides functions and an interactive app for exploring, extracting, and ranking features using data-based and model-based techniques, including statistical, spectral, and time-series analysis.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
Automated Driving Toolbox™ is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
Navigation Toolbox™ is a tool that provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-based path planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
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Computer Vision is a field of Artificial Intelligence (AI) that focuses on enabling computers to identify and understand objects and people in images and videos.
OpenCV Courses
Exploring Computer Vision in Microsoft Azure
Top Computer Vision Courses Online | Coursera
Top Computer Vision Courses Online | Udemy
Learn Computer Vision with Online Courses and Lessons | edX
Computer Vision and Image Processing Fundamentals | edX
Introduction to Computer Vision Courses | Udacity
Computer Vision Nanodegree program | Udacity
Machine Vision Course |MIT Open Courseware
Computer Vision Training Courses | NobleProg
Visual Computing Graduate Program | Stanford Online
OpenCV is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Microsoft Computer Vision Recipes is a project that provides examples and best practice guidelines for building computer vision systems. This allows people to build a comprehensive set of tools and examples that leverage recent advances in Computer Vision algorithms, neural architectures, and operationalizing such systems. Creatin from existing state-of-the-art libraries and build additional utility around loading image data, optimizing and evaluating models, and scaling up to the cloud.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
Automated Driving Toolbox™ is a MATLAB tool that provides algorithms and tools for designing, simulating, and testing ADAS and autonomous driving systems. You can design and test vision and lidar perception systems, as well as sensor fusion, path planning, and vehicle controllers. Visualization tools include a bird’s-eye-view plot and scope for sensor coverage, detections and tracks, and displays for video, lidar, and maps. The toolbox lets you import and work with HERE HD Live Map data and OpenDRIVE® road networks. It also provides reference application examples for common ADAS and automated driving features, including FCW, AEB, ACC, LKA, and parking valet. The toolbox supports C/C++ code generation for rapid prototyping and HIL testing, with support for sensor fusion, tracking, path planning, and vehicle controller algorithms.
LRSLibrary is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Statistics and Machine Learning Toolbox™ is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
Partial Differential Equation Toolbox™ is a tool that provides functions for solving structural mechanics, heat transfer, and general partial differential equations (PDEs) using finite element analysis.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
Data Acquisition Toolbox™ is a tool that provides apps and functions for configuring data acquisition hardware, reading data into MATLAB® and Simulink®, and writing data to DAQ analog and digital output channels. The toolbox supports a variety of DAQ hardware, including USB, PCI, PCI Express®, PXI®, and PXI Express® devices, from National Instruments® and other vendors.
Microsoft AirSim is a simulator for drones, cars and more, built on Unreal Engine (with an experimental Unity release). AirSim is open-source, cross platform, and supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim is being developed as a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles.
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Natural Language Processing (NLP) is a branch of artificial intelligence (AI) focused on giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics rule-based modeling of human language with statistical, machine learning, and deep learning models.
Natural Language Processing With Python's NLTK Package
Cognitive Services—APIs for AI Developers | Microsoft Azure
Artificial Intelligence Services - Amazon Web Services (AWS)
Google Cloud Natural Language API
Top Natural Language Processing Courses Online | Udemy
Introduction to Natural Language Processing (NLP) | Udemy
Top Natural Language Processing Courses | Coursera
Natural Language Processing | Coursera
Natural Language Processing in TensorFlow | Coursera
Learn Natural Language Processing with Online Courses and Lessons | edX
Build a Natural Language Processing Solution with Microsoft Azure | Pluralsight
Natural Language Processing (NLP) Training Courses | NobleProg
Natural Language Processing with Deep Learning Course | Standford Online
Advanced Natural Language Processing - MIT OpenCourseWare
Certified Natural Language Processing Expert Certification | IABAC
Natural Language Processing Course - Intel
Natural Language Toolkit (NLTK) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries.
spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pretrained pipelines and currently supports tokenization and training for 60+ languages. It also features neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT.
CoreNLP is a set of natural language analysis tools written in Java. CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations.
NLPnet is a Python library for Natural Language Processing tasks based on neural networks. It performs part-of-speech tagging, semantic role labeling and dependency parsing.
Flair is a simple framework for state-of-the-art Natural Language Processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), special support for biomedical data, sense disambiguation and classification, with support for a rapidly growing number of languages.
Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models.
Apache OpenNLP is an open-source library for a machine learning based toolkit used in the processing of natural language text. It features an API for use cases like Named Entity Recognition, Sentence Detection, POS(Part-Of-Speech) tagging, Tokenization Feature extraction, Chunking, Parsing, and Coreference resolution.
Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Tensorflow_macOS is a Mac-optimized version of TensorFlow and TensorFlow Addons for macOS 11.0+ accelerated using Apple's ML Compute framework.
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.
PyTorch is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference.
Anaconda is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.
PlaidML is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.
Scikit-Learn is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Apache Airflow is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Open Neural Network Exchange(ONNX) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
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Bioinformatics is a field of computational science that has to do with the analysis of sequences of biological molecules. This usually refers to genes, DNA, RNA, or protein, and is particularly useful in comparing genes and other sequences in proteins and other sequences within an organism or between organisms, looking at evolutionary relationships between organisms, and using the patterns that exist across DNA and protein sequences to figure out what their function is.
European Bioinformatics Institute
National Center for Biotechnology Information
Online Courses in Bioinformatics |ISCB - International Society for Computational Biology
Bioinformatics | Coursera
Top Bioinformatics Courses | Udemy
Biometrics Courses | Udemy
Learn Bioinformatics with Online Courses and Lessons | edX
Bioinformatics Graduate Certificate | Harvard Extension School
Bioinformatics and Biostatistics | UC San Diego Extension
Bioinformatics and Proteomics - Free Online Course Materials | MIT
Introduction to Biometrics course - Biometrics Institute
Bioconductor is an open source project that provides tools for the analysis and comprehension of high-throughput genomic data. Bioconductor uses the R statistical programming language, and is open source and open development. It has two releases each year, and an active user community. Bioconductor is also available as an AMI (Amazon Machine Image) and Docker images.
Bioconda is a channel for the conda package manager specializing in bioinformatics software. It has a repository of packages containing over 7000 bioinformatics packages ready to use with conda install.
UniProt is a freely accessible database that provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information.
Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (mammalian) genomes.
Biopython is a set of freely available tools for biological computation written in Python by an international team of developers. It is a distributed collaborative effort to develop Python libraries and applications which address the needs of current and future work in bioinformatics.
BioRuby is a toolkit that has components for sequence analysis, pathway analysis, protein modelling and phylogenetic analysis; it supports many widely used data formats and provides easy access to databases, external programs and public web services, including BLAST, KEGG, GenBank, MEDLINE and GO.
BioJava is a toolkit that provides an API to maintain local installations of the PDB, load and manipulate structures, perform standard analysis such as sequence and structure alignments and visualize them in 3D.
BioPHP is an open source project that provides a collection of open source PHP code, with classes for DNA and protein sequence analysis, alignment, database parsing, and other bioinformatics tools.
Avogadro is an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible high quality rendering and a powerful plugin architecture.
Ascalaph Designer is a program for molecular dynamic simulations. Under a single graphical environment are represented as their own implementation of molecular dynamics as well as the methods of classical and quantum mechanics of popular programs.
Anduril is a workflow platform for analyzing large data sets. Anduril provides facilities for analyzing high-thoughput data in biomedical research, and the platform is fully extensible by third parties. Ready-made tools support data visualization, DNA/RNA/ChIP-sequencing, DNA/RNA microarrays, cytometry and image analysis.
Galaxy is an open source, web-based platform for accessible, reproducible, and transparent computational biomedical research. It allows users without programming experience to easily specify parameters and run individual tools as well as larger workflows. It also captures run information so that any user can repeat and understand a complete computational analysis.
PathVisio is a free open-source pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. It is developed in Java and can be extended with plugins.
Orange is a powerful data mining and machine learning toolkit that performs data analysis and visualization.
Basic Local Alignment Search Tool is a tool that finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance.
OSIRIS is public-domain, free, and open source STR analysis software designed for clinical, forensic, and research use, and has been validated for use as an expert system for single-source samples.
NCBI BioSystems is a Database that provides integrated access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez.
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CUDA Toolkit. Source: NVIDIA Developer CUDA
CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. In GPU-accelerated applications, the sequential part of the workload runs on the CPU, which is optimized for single-threaded. The compute intensive portion of the application runs on thousands of GPU cores in parallel. When using CUDA, developers can program in popular languages such as C, C++, Fortran, Python and MATLAB.
CUDA Toolkit Documentation
CUDA Quick Start Guide
CUDA on WSL
CUDA GPU support for TensorFlow
NVIDIA Deep Learning cuDNN Documentation
NVIDIA GPU Cloud Documentation
NVIDIA NGC is a hub for GPU-optimized software for deep learning, machine learning, and high-performance computing (HPC) workloads.
NVIDIA NGC Containers is a registry that provides researchers, data scientists, and developers with simple access to a comprehensive catalog of GPU-accelerated software for AI, machine learning and HPC. These containers take full advantage of NVIDIA GPUs on-premises and in the cloud.
CUDA Toolkit is a collection of tools & libraries that provide a development environment for creating high performance GPU-accelerated applications. The CUDA Toolkit allows you can develop, optimize, and deploy your applications on GPU-accelerated embedded systems, desktop workstations, enterprise data centers, cloud-based platforms and HPC supercomputers. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ compiler, and a runtime library to build and deploy your application on major architectures including x86, Arm and POWER.
NVIDIA cuDNN is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including Caffe2, Chainer, Keras, MATLAB, MxNet, PyTorch, and TensorFlow.
CUDA-X HPC is a collection of libraries, tools, compilers and APIs that help developers solve the world's most challenging problems. CUDA-X HPC includes highly tuned kernels essential for high-performance computing (HPC).
NVIDIA Container Toolkit is a collection of tools & libraries that allows users to build and run GPU accelerated Docker containers. The toolkit includes a container runtime library and utilities to automatically configure containers to leverage NVIDIA GPUs.
Minkowski Engine is an auto-differentiation library for sparse tensors. It supports all standard neural network layers such as convolution, pooling, unpooling, and broadcasting operations for sparse tensors.
CUTLASS is a collection of CUDA C++ template abstractions for implementing high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement similar to those used to implement cuBLAS.
CUB is a cooperative primitives for CUDA C++ kernel authors.
Tensorman is a utility for easy management of Tensorflow containers by developed by System76.Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.
Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.
Chainer is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using CuPy for high performance training and inference.
CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. It supports a subset of numpy.ndarray interface.
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the details of CUDA programming.
cuML is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.
ArrayFire is a general-purpose library that simplifies the process of developing software that targets parallel and massively-parallel architectures including CPUs, GPUs, and other hardware acceleration devices.
Thrust is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs.
AresDB is a GPU-powered real-time analytics storage and query engine. It features low query latency, high data freshness and highly efficient in-memory and on disk storage management.
Arraymancer is a tensor (N-dimensional array) project in Nim. The main focus is providing a fast and ergonomic CPU, Cuda and OpenCL ndarray library on which to build a scientific computing ecosystem.
Kintinuous is a real-time dense visual SLAM system capable of producing high quality globally consistent point and mesh reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor.
GraphVite is a general graph embedding engine, dedicated to high-speed and large-scale embedding learning in various applications.
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MATLAB is a programming language that does numerical computing such as expressing matrix and array mathematics directly.
MATLAB Documentation
Getting Started with MATLAB
MATLAB and Simulink Training from MATLAB Academy
MathWorks Certification Program
MATLAB Online Courses from Udemy
MATLAB Online Courses from Coursera
MATLAB Online Courses from edX
Building a MATLAB GUI
MATLAB Style Guidelines 2.0
Setting Up Git Source Control with MATLAB & Simulink
Pull, Push and Fetch Files with Git with MATLAB & Simulink
Create New Repository with MATLAB & Simulink
PRMLT is Matlab code for machine learning algorithms in the PRML book.
MATLAB and Simulink Services & Applications List
MATLAB in the Cloud is a service that allows you to run in cloud environments from MathWorks Cloud to Public Clouds including AWS and Azure.
MATLAB Online™ is a service that allows to users to uilitize MATLAB and Simulink through a web browser such as Google Chrome.
Simulink is a block diagram environment for Model-Based Design. It supports simulation, automatic code generation, and continuous testing of embedded systems.
Simulink Online™ is a service that provides access to Simulink through your web browser.
MATLAB Drive™ is a service that gives you the ability to store, access, and work with your files from anywhere.
MATLAB Parallel Server™ is a tool that lets you scale MATLAB® programs and Simulink® simulations to clusters and clouds. You can prototype your programs and simulations on the desktop and then run them on clusters and clouds without recoding. MATLAB Parallel Server supports batch jobs, interactive parallel computations, and distributed computations with large matrices.
MATLAB Schemer is a MATLAB package makes it easy to change the color scheme (theme) of the MATLAB display and GUI.
LRSLibrary is a Low-Rank and Sparse Tools for Background Modeling and Subtraction in Videos. The library was designed for moving object detection in videos, but it can be also used for other computer vision and machine learning problems.
Image Processing Toolbox™ is a tool that provides a comprehensive set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing.
Computer Vision Toolbox™ is a tool that provides algorithms, functions, and apps for designing and testing computer vision, 3D vision, and video processing systems. You can perform object detection and tracking, as well as feature detection, extraction, and matching. You can automate calibration workflows for single, stereo, and fisheye cameras. For 3D vision, the toolbox supports visual and point cloud SLAM, stereo vision, structure from motion, and point cloud processing.
Statistics and Machine Learning Toolbox™ is a tool that provides functions and apps to describe, analyze, and model data. You can use descriptive statistics, visualizations, and clustering for exploratory data analysis; fit probability distributions to data; generate random numbers for Monte Carlo simulations, and perform hypothesis tests. Regression and classification algorithms let you draw inferences from data and build predictive models either interactively, using the Classification and Regression Learner apps, or programmatically, using AutoML.
Lidar Toolbox™ is a tool that provides algorithms, functions, and apps for designing, analyzing, and testing lidar processing systems. You can perform object detection and tracking, semantic segmentation, shape fitting, lidar registration, and obstacle detection. Lidar Toolbox supports lidar-camera cross calibration for workflows that combine computer vision and lidar processing.
Mapping Toolbox™ is a tool that provides algorithms and functions for transforming geographic data and creating map displays. You can visualize your data in a geographic context, build map displays from more than 60 map projections, and transform data from a variety of sources into a consistent geographic coordinate system.
UAV Toolbox is an application that provides tools and reference applications for designing, simulating, testing, and deploying unmanned aerial vehicle (UAV) and drone applications. You can design autonomous flight algorithms, UAV missions, and flight controllers. The Flight Log Analyzer app lets you interactively analyze 3D flight paths, telemetry information, and sensor readings from common flight log formats.
Parallel Computing Toolbox™ is a tool that lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs such as parallel for-loops, special array types, and parallelized numerical algorithms enable you to parallelize MATLAB® applications without CUDA or MPI programming. The toolbox lets you use parallel-enabled functions in MATLAB and other toolboxes. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. Programs and models can run in both interactive and batch modes.
Partial Differential Equation Toolbox™ is a tool that provides functions for solving structural mechanics, heat transfer, and general partial differential equations (PDEs) using finite element analysis.
ROS Toolbox is a tool that provides an interface connecting MATLAB® and Simulink® with the Robot Operating System (ROS and ROS 2), enabling you to create a network of ROS nodes. The toolbox includes MATLAB functions and Simulink blocks to import, analyze, and play back ROS data recorded in rosbag files. You can also connect to a live ROS network to access ROS messages.
Robotics Toolbox™ provides a toolbox that brings robotics specific functionality(designing, simulating, and testing manipulators, mobile robots, and humanoid robots) to MATLAB, exploiting the native capabilities of MATLAB (linear algebra, portability, graphics). The toolbox also supports mobile robots with functions for robot motion models (bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. The Toolbox also including a detailed Simulink model for a quadrotor flying robot.
Deep Learning Toolbox™ is a tool that provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. It can exchange models with TensorFlow™ and PyTorch through the ONNX format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
Reinforcement Learning Toolbox™ is a tool that provides an app, functions, and a Simulink® block for training policies using reinforcement learning algorithms, including DQN, PPO, SAC, and DDPG. You can use these policies to implement controllers and decision-making algorithms for complex applications such as resource allocation, robotics, and autonomous systems.
Deep Learning HDL Toolbox™ is a tool that provides functions and tools to prototype and implement deep learning networks on FPGAs and SoCs. It provides pre-built bitstreams for running a variety of deep learning networks on supported Xilinx® and Intel® FPGA and SoC devices. Profiling and estimation tools let you customize a deep learning network by exploring design, performance, and resource utilization tradeoffs.
Model Predictive Control Toolbox™ is a tool that provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
Vision HDL Toolbox™ is a tool that provides pixel-streaming algorithms for the design and implementation of vision systems on FPGAs and ASICs. It provides a design framework that supports a diverse set of interface types, frame sizes, and frame rates. The image processing, video, and computer vision algorithms in the toolbox use an architecture appropriate for HDL implementations.
SoC Blockset™ is a tool that provides Simulink® blocks and visualization tools for modeling, simulating, and analyzing hardware and software architectures for ASICs, FPGAs, and systems on a chip (SoC). You can build your system architecture using memory models, bus models, and I/O models, and simulate the architecture together with the algorithms.
Wireless HDL Toolbox™ is a tool that provides pre-verified, hardware-ready Simulink® blocks and subsystems for developing 5G, LTE, and custom OFDM-based wireless communication applications. It includes reference applications, IP blocks, and gateways between frame and sample-based processing.
ThingSpeak™ is an IoT analytics service that allows you to aggregate, visualize, and analyze live data streams in the cloud. ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak, you can perform online analysis and process data as it comes in. ThingSpeak is often used for prototyping and proof-of-concept IoT systems that require analytics.
SEA-MAT is a collaborative effort to organize and distribute Matlab tools for the Oceanographic Community.
Gramm is a complete data visualization toolbox for Matlab. It provides an easy to use and high-level interface to produce publication-quality plots of complex data with varied statistical visualizations. Gramm is inspired by R's ggplot2 library.
hctsa is a software package for running highly comparative time-series analysis using Matlab.
Plotly is a Graphing Library for MATLAB.
YALMIP is a MATLAB toolbox for optimization modeling.
GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation.
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C++ is a cross-platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
C is a general-purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.
Embedded C is a set of language extensions for the C programming language by the C Standards Committee to address issues that exist between C extensions for different embedded systems. The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.
C & C++ Developer Tools from JetBrains
Open source C++ libraries on cppreference.com
C++ Graphics libraries
C++ Libraries in MATLAB
C++ Tools and Libraries Articles
Google C++ Style Guide
Introduction C++ Education course on Google Developers
C++ style guide for Fuchsia
C and C++ Coding Style Guide by OpenTitan
Chromium C++ Style Guide
C++ Core Guidelines
C++ Style Guide for ROS
Learn C++
Learn C : An Interactive C Tutorial
C++ Institute
C++ Online Training Courses on LinkedIn Learning
C++ Tutorials on W3Schools
Learn C Programming Online Courses on edX
Learn C++ with Online Courses on edX
Learn C++ on Codecademy
Coding for Everyone: C and C++ course on Coursera
C++ For C Programmers on Coursera
Top C Courses on Coursera
C++ Online Courses on Udemy
Top C Courses on Udemy
Basics of Embedded C Programming for Beginners on Udemy
C++ For Programmers Course on Udacity
C++ Fundamentals Course on Pluralsight
Introduction to C++ on MIT Free Online Course Materials
Introduction to C++ for Programmers | Harvard
Online C Courses | Harvard University
AWS SDK for C++
Azure SDK for C++
Azure SDK for C
C++ Client Libraries for Google Cloud Services
Visual Studio is an integrated development environment (IDE) from Microsoft; which is a feature-rich application that can be used for many aspects of software development. Visual Studio makes it easy to edit, debug, build, and publish your app. By using Microsoft software development platforms such as Windows API, Windows Forms, Windows Presentation Foundation, and Windows Store.
Visual Studio Code is a code editor redefined and optimized for building and debugging modern web and cloud applications.
Vcpkg is a C++ Library Manager for Windows, Linux, and MacOS.
ReSharper C++ is a Visual Studio Extension for C++ developers developed by JetBrains.
AppCode is constantly monitoring the quality of your code. It warns you of errors and smells and suggests quick-fixes to resolve them automatically. AppCode provides lots of code inspections for Objective-C, Swift, C/C++, and a number of code inspections for other supported languages. All code inspections are run on the fly.
CLion is a cross-platform IDE for C and C++ developers developed by JetBrains.
Code::Blocks is a free C/C++ and Fortran IDE built to meet the most demanding needs of its users. It is designed to be very extensible and fully configurable. Built around a plugin framework, Code::Blocks can be extended with plugins.
CppSharp is a tool and set of libraries which facilitates the usage of native C/C++ code with the .NET ecosystem. It consumes C/C++ header and library files and generates the necessary glue code to surface the native API as a managed API. Such an API can be used to consume an existing native library in your managed code or add managed scripting support to a native codebase.
Conan is an Open Source Package Manager for C++ development and dependency management into the 21st century and on par with the other development ecosystems.
High Performance Computing (HPC) SDK is a comprehensive toolbox for GPU accelerating HPC modeling and simulation applications. It includes the C, C++, and Fortran compilers, libraries, and analysis tools necessary for developing HPC applications on the NVIDIA platform.
Thrust is a C++ parallel programming library which resembles the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.
Boost is an educational opportunity focused on cutting-edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.
Automake is a tool for automatically generating Makefile.in files compliant with the GNU Coding Standards. Automake requires the use of GNU Autoconf.
Cmake is an open-source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
GDB is a debugger, that allows you to see what is going on `inside' another program while it executes or what another program was doing at the moment it crashed.
GCC is a compiler Collection that includes front ends for C, C++, Objective-C, Fortran, Ada, Go, and D, as well as libraries for these languages.
GSL is a numerical library for C and C++ programmers. It is free software under the GNU General Public License. The library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. There are over 1000 functions in total with an extensive test suite.
OpenGL Extension Wrangler Library (GLEW) is a cross-platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.
Libtool is a generic library support script that hides the complexity of using shared libraries behind a consistent, portable interface. To use Libtool, add the new generic library building commands to your Makefile, Makefile.in, or Makefile.am.
Maven is a software project management and comprehension tool. Based on the concept of a project object model (POM), Maven can manage a project's build, reporting and documentation from a central piece of information.
TAU (Tuning And Analysis Utilities) is capable of gathering performance information through instrumentation of functions, methods, basic blocks, and statements as well as event-based sampling. All C++ language features are supported including templates and namespaces.
Clang is a production quality C, Objective-C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.
OpenCV is a highly optimized library with focus on real-time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
Libcu++ is the NVIDIA C++ Standard Library for your entire system. It provides a heterogeneous implementation of the C++ Standard Library that can be used in and between CPU and GPU code.
ANTLR (ANother Tool for Language Recognition) is a powerful parser generator for reading, processing, executing, or translating structured text or binary files. It's widely used to build languages, tools, and frameworks. From a grammar, ANTLR generates a parser that can build parse trees and also generates a listener interface that makes it easy to respond to the recognition of phrases of interest.
Oat++ is a light and powerful C++ web framework for highly scalable and resource-efficient web application. It's zero-dependency and easy-portable.
JavaCPP is a program that provides efficient access to native C++ inside Java, not unlike the way some C/C++ compilers interact with assembly language.
Cython is a language that makes writing C extensions for Python as easy as Python itself. Cython is based on Pyrex, but supports more cutting edge functionality and optimizations such as calling C functions and declaring C types on variables and class attributes.
Spdlog is a very fast, header-only/compiled, C++ logging library.
Infer is a static analysis tool for Java, C++, Objective-C, and C. Infer is written in OCaml.
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Java is a popular programming language and development platform(JDK). It reduces costs, shortens development timeframes, drives innovation, and improves application services. With millions of developers running more than 51 billion Java Virtual Machines worldwide.
The Eclipse Foundation is home to a worldwide community of developers, the Eclipse IDE, Jakarta EE and over 375 open source projects, including runtimes, tools and frameworks for Java and other languages.
Getting Started with Java
Oracle Java certifications from Oracle University
Google Developers Training
Google Developers Certification
Java Tutorial by W3Schools
Building Your First Android App in Java
Getting Started with Java in Visual Studio Code
Google Java Style Guide
AOSP Java Code Style for Contributors
Chromium Java style guide
Get Started with OR-Tools for Java
Getting started with Java Tool Installer task for Azure Pipelines
Gradle User Manual
Java SE contains several tools to assist in program development and debugging, and in the monitoring and troubleshooting of production applications.
JDK Development Tools includes the Java Web Start Tools (javaws) Java Troubleshooting, Profiling, Monitoring and Management Tools (jcmd, jconsole, jmc, jvisualvm); and Java Web Services Tools (schemagen, wsgen, wsimport, xjc).
Android Studio is the official integrated development environment for Google's Android operating system, built on JetBrains' IntelliJ IDEA software and designed specifically for Android development. Availble on Windows, macOS, Linux, Chrome OS.
IntelliJ IDEA is an IDE for Java, but it also understands and provides intelligent coding assistance for a large variety of other languages such as Kotlin, SQL, JPQL, HTML, JavaScript, etc., even if the language expression is injected into a String literal in your Java code.
NetBeans is an IDE provides Java developers with all the tools needed to create professional desktop, mobile and enterprise applications. Creating, Editing, and Refactoring. The IDE provides wizards and templates to let you create Java EE, Java SE, and Java ME applications.
Java Design Patterns is a collection of the best formalized practices a programmer can use to solve common problems when designing an application or system.
Elasticsearch is a distributed RESTful search engine built for the cloud written in Java.
RxJava is a Java VM implementation of Reactive Extensions: a library for composing asynchronous and event-based programs by using observable sequences. It extends the observer pattern to support sequences of data/events and adds operators that allow you to compose sequences together declaratively while abstracting away concerns about things like low-level threading, synchronization, thread-safety and concurrent data structures.
Guava is a set of core Java libraries from Google that includes new collection types (such as multimap and multiset), immutable collections, a graph library, and utilities for concurrency, I/O, hashing, caching, primitives, strings, and more! It is widely used on most Java projects within Google, and widely used by many other companies as well.
okhttp is a HTTP client for Java and Kotlin developed by Square.
Retrofit is a type-safe HTTP client for Android and Java develped by Square.
LeakCanary is a memory leak detection library for Android develped by Square.
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Flink is an open source stream processing framework with powerful stream- and batch-processing capabilities with elegant and fluent APIs in Java and Scala.
Fastjson is a Java library that can be used to convert Java Objects into their JSON representation. It can also be used to convert a JSON string to an equivalent Java object.
libGDX is a cross-platform Java game development framework based on OpenGL (ES) that works on Windows, Linux, Mac OS X, Android, your WebGL enabled browser and iOS.
Jenkins is the leading open-source automation server. Built with Java, it provides over 1700 plugins to support automating virtually anything, so that humans can actually spend their time doing things machines cannot.
DBeaver is a free multi-platform database tool for developers, SQL programmers, database administrators and analysts. Supports any database which has JDBC driver (which basically means - ANY database). EE version also supports non-JDBC datasources (MongoDB, Cassandra, Redis, DynamoDB, etc).
Redisson is a Redis Java client with features of In-Memory Data Grid. Over 50 Redis based Java objects and services: Set, Multimap, SortedSet, Map, List, Queue, Deque, Semaphore, Lock, AtomicLong, Map Reduce, Publish / Subscribe, Bloom filter, Spring Cache, Tomcat, Scheduler, JCache API, Hibernate, MyBatis, RPC, and local cache.
GraalVM is a universal virtual machine for running applications written in JavaScript, Python, Ruby, R, JVM-based languages like Java, Scala, Clojure, Kotlin, and LLVM-based languages such as C and C++.
Gradle is a build automation tool for multi-language software development. From mobile apps to microservices, from small startups to big enterprises, Gradle helps teams build, automate and deliver better software, faster. Write in Java, C++, Python or your language of choice.
Apache Groovy is a powerful, optionally typed and dynamic language, with static-typing and static compilation capabilities, for the Java platform aimed at improving developer productivity thanks to a concise, familiar and easy to learn syntax. It integrates smoothly with any Java program, and immediately delivers to your application powerful features, including scripting capabilities, Domain-Specific Language authoring, runtime and compile-time meta-programming and functional programming.
JaCoCo is a free code coverage library for Java, which has been created by the EclEmma team based on the lessons learned from using and integration existing libraries for many years.
Apache JMeter is used to test performance both on static and dynamic resources, Web dynamic applications. It also used to simulate a heavy load on a server, group of servers, network or object to test its strength or to analyze overall performance under different load types.
Junit is a simple framework to write repeatable tests. It is an instance of the xUnit architecture for unit testing frameworks.
Mockito is the most popular Mocking framework for unit tests written in Java.
SpotBugs is a program which uses static analysis to look for bugs in Java code.
SpringBoot is a great tool that helps you to create Spring-powered, production-grade applications and services with absolute minimum fuss. It takes an opinionated view of the Spring platform so that new and existing users can quickly get to the bits they need.
YourKit is a technology leader, creator of the most innovative and intelligent tools for profiling Java & .NET applications.
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Python is an interpreted, high-level programming language. Python is used heavily in the fields of Data Science and Machine Learning.
Python Developer’s Guide is a comprehensive resource for contributing to Python – for both new and experienced contributors. It is maintained by the same community that maintains Python.
Azure Functions Python developer guide is an introduction to developing Azure Functions using Python. The content below assumes that you've already read the Azure Functions developers guide.
CheckiO is a programming learning platform and a gamified website that teaches Python through solving code challenges and competing for the most elegant and creative solutions.
Python Institute
PCEP – Certified Entry-Level Python Programmer certification
PCAP – Certified Associate in Python Programming certification
PCPP – Certified Professional in Python Programming 1 certification
PCPP – Certified Professional in Python Programming 2
MTA: Introduction to Programming Using Python Certification
Getting Started with Python in Visual Studio Code
Google's Python Style Guide
Google's Python Education Class
Real Python
The Python Open Source Computer Science Degree by Forrest Knight
Intro to Python for Data Science
Intro to Python by W3schools
Codecademy's Python 3 course
Learn Python with Online Courses and Classes from edX
Python Courses Online from Coursera
Python Package Index (PyPI) is a repository of software for the Python programming language. PyPI helps you find and install software developed and shared by the Python community.
PyCharm is the best IDE I've ever used. With PyCharm, you can access the command line, connect to a database, create a virtual environment, and manage your version control system all in one place, saving time by avoiding constantly switching between windows.
Python Tools for Visual Studio(PTVS) is a free, open source plugin that turns Visual Studio into a Python IDE. It supports editing, browsing, IntelliSense, mixed Python/C++ debugging, remote Linux/MacOS debugging, profiling, IPython, and web development with Django and other frameworks.
Pylance is an extension that works alongside Python in Visual Studio Code to provide performant language support. Under the hood, Pylance is powered by Pyright, Microsoft's static type checking tool.
Pyright is a fast type checker meant for large Python source bases. It can run in a “watch” mode and performs fast incremental updates when files are modified.
Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design.
Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries.
Web2py is an open-source web application framework written in Python allowing allows web developers to program dynamic web content. One web2py instance can run multiple web sites using different databases.
AWS Chalice is a framework for writing serverless apps in python. It allows you to quickly create and deploy applications that use AWS Lambda.
Tornado is a Python web framework and asynchronous networking library. Tornado uses a non-blocking network I/O, which can scale to tens of thousands of open connections.
HTTPie is a command line HTTP client that makes CLI interaction with web services as easy as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers.
Scrapy is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.
Sentry is a service that helps you monitor and fix crashes in realtime. The server is in Python, but it contains a full API for sending events from any language, in any application.
Pipenv is a tool that aims to bring the best of all packaging worlds (bundler, composer, npm, cargo, yarn, etc.) to the Python world.
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Bottle is a fast, simple and lightweight WSGI micro web-framework for Python. It is distributed as a single file module and has no dependencies other than the Python Standard Library.
CherryPy is a minimalist Python object-oriented HTTP web framework.
Sanic is a Python 3.6+ web server and web framework that's written to go fast.
Pyramid is a small and fast open source Python web framework. It makes real-world web application development and deployment more fun and more productive.
TurboGears is a hybrid web framework able to act both as a Full Stack framework or as a Microframework.
Falcon is a reliable, high-performance Python web framework for building large-scale app backends and microservices with support for MongoDB, Pluggable Applications and autogenerated Admin.
Neural Network Intelligence(NNI) is an open source AutoML toolkit for automate machine learning lifecycle, including Feature Engineering, Neural Architecture Search, Model Compression and Hyperparameter Tuning.
Dash is a popular Python framework for building ML & data science web apps for Python, R, Julia, and Jupyter.
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built-in.
Locust is an easy to use, scriptable and scalable performance testing tool.
spaCy is a library for advanced Natural Language Processing in Python and Cython.
NumPy is the fundamental package needed for scientific computing with Python.
Pillow is a friendly PIL(Python Imaging Library) fork.
IPython is a command shell for interactive computing in multiple programming languages, originally developed for the Python programming language, that offers enhanced introspection, rich media, additional shell syntax, tab completion, and rich history.
GraphLab Create is a Python library, backed by a C++ engine, for quickly building large-scale, high-performance machine learning models.
Pandas is a fast, powerful, and easy to use open source data structrures, data analysis and manipulation tool, built on top of the Python programming language.
PuLP is an Linear Programming modeler written in python. PuLP can generate LP files and call on use highly optimized solvers, GLPK, COIN CLP/CBC, CPLEX, and GUROBI, to solve these linear problems.
Matplotlib is a 2D plotting library for creating static, animated, and interactive visualizations in Python. Matplotlib produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.
Scikit-Learn is a simple and efficient tool for data mining and data analysis. It is built on NumPy,SciPy, and mathplotlib.
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Scala is a combination of object-oriented and functional programming in one concise, high-level language. Scala's static types help avoid bugs in complex applications, and its JVM and JavaScript runtimes let you build high-performance systems with easy access to huge ecosystems of libraries.
Scala Style Guide
Databricks Scala Style Guide
Data Science using Scala and Spark on Azure
Creating a Scala Maven application for Apache Spark in HDInsight using IntelliJ
Intro to Spark DataFrames using Scala with Azure Databricks
Using Scala to Program AWS Glue ETL Scripts
Using Flink Scala shell with Amazon EMR clusters
AWS EMR and Spark 2 using Scala from Udemy
Using the Google Cloud Storage connector with Apache Spark
Write and run Spark Scala jobs on Cloud Dataproc for Google Cloud
Scala Courses and Certifications from edX
Scala Courses from Coursera
Top Scala Courses from Udemy
Apache Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.
Apache Spark Connector for SQL Server and Azure SQL is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.
Azure Databricks is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.
Apache PredictionIO is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.
Cluster Manager for Apache Kafka(CMAK) is a tool for managing Apache Kafka clusters.
BigDL is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.
Eclipse Deeplearning4J (DL4J) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.
Play Framework is a web framework combines productivity and performance making it easy to build scalable web applications with Java and Scala.
Dotty is a research compiler that will become Scala 3.
AWScala is a tool that enables Scala developers to easily work with Amazon Web Services in the Scala way.
Scala.js is a compiler that converts Scala to JavaScript.
Polynote is an experimental polyglot notebook environment. Currently, it supports Scala and Python (with or without Spark), SQL, and Vega.
Scala Native is an optimizing ahead-of-time compiler and lightweight managed runtime designed specifically for Scala.
Gitbucket is a Git platform powered by Scala with easy installation, high extensibility & GitHub API compatibility.
Finagle is a fault tolerant, protocol-agnostic RPC system
Gatling is a load test tool. It officially supports HTTP, WebSocket, Server-Sent-Events and JMS.
Scalatra is a tiny Scala high-performance, async web framework, inspired by Sinatra.
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R is an open source software environment for statistical computing and graphics. It compiles and runs on a wide variety of platforms such as Windows and MacOS.
An Introduction to R
Google's R Style Guide
R developer's guide to Azure
Running R at Scale on Google Compute Engine
Running R on AWS
RStudio Server Pro for AWS
Learn R by Codecademy
Learn R Programming with Online Courses and Lessons by edX
R Language Courses by Coursera
Learn R For Data Science by Udacity
RStudio is an integrated development environment for R and Python, with a console, syntax-highlighting editor that supports direct code execution, and tools for plotting, history, debugging and workspace management.
Shiny is a newer package from RStudio that makes it incredibly easy to build interactive web applications with R.
Rmarkdown is a package helps you create dynamic analysis documents that combine code, rendered output (such as figures), and prose.
Rplugin is R Language supported plugin for the IntelliJ IDE.
Plotly is an R package for creating interactive web graphics via the open source JavaScript graphing library plotly.js.
Metaflow is a Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning.
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data.
LightGBM is a gradient boosting framework that uses tree based learning algorithms, used for ranking, classification and many other machine learning tasks.
Dash is a Python framework for building analytical web applications in Python, R, Julia, and Jupyter.
MLR is Machine Learning in R.
ML workspace is an all-in-one web-based IDE specialized for machine learning and data science. It is simple to deploy and gets you started within minutes to productively built ML solutions on your own machines. ML workspace is the ultimate tool for developers preloaded with a variety of popular data science libraries (Tensorflow, PyTorch, Keras, and MXnet) and dev tools (Jupyter, VS Code, and Tensorboard) perfectly configured, optimized, and integrated.
CatBoost is a fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Plumber is a tool that allows you to create a web API by merely decorating your existing R source code with special comments.
Drake is an R-focused pipeline toolkit for reproducibility and high-performance computing.
DiagrammeR is a package you can create, modify, analyze, and visualize network graph diagrams. The output can be incorporated into R Markdown documents, integrated with Shiny web apps, converted to other graph formats, or exported as image files.
Knitr is a general-purpose literate programming engine in R, with lightweight API's designed to give users full control of the output without heavy coding work.
Broom is a tool that converts statistical analysis objects from R into tidy format.
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Julia is a high-level, high-performance dynamic language for technical computing. Julia programs compile to efficient native code for multiple platforms via LLVM.
JuliaHub contains over 4,000 Julia packages for use by the community.
Julia Observer
Julia Manual
JuliaLang Essentials
Julia Style Guide
Julia By Example
JuliaLang Gitter
DataFrames Tutorial using Jupyter Notebooks
Julia Academy
Julia Meetup groups
Julia on Microsoft Azure
JuliaPro is a free and fast way to setup Julia for individual researchers, engineers, scientists, quants, traders, economists, students and others. Julia developers can build better software quicker and easier while benefiting from Julia's unparalleled high performance. It includes 2600+ open source packages or from a curated list of 250+ JuliaPro packages. Curated packages are tested, documented and supported by Julia Computing.
Juno is a powerful, free IDE based on Atom for the Julia language.
Debugger.jl is the Julia debuggin tool.
Profile (Stdlib) is a module provides tools to help developers improve the performance of their code. When used, it takes measurements on running code, and produces output that helps you understand how much time is spent on individual line's.
Revise.jl allows you to modify code and use the changes without restarting Julia. With Revise, you can be in the middle of a session and then update packages, switch git branches, and/or edit the source code in the editor of your choice; any changes will typically be incorporated into the very next command you issue from the REPL. This can save you the overhead of restarting Julia, loading packages, and waiting for code to JIT-compile.
JuliaGPU is a Github organization created to unify the many packages for programming GPUs in Julia. With its high-level syntax and flexible compiler, Julia is well positioned to productively program hardware accelerators like GPUs without sacrificing performance.
IJulia.jl is the Julia kernel for Jupyter.
AWS.jl is a Julia interface for Amazon Web Services.
CUDA.jl is a package for the main programming interface for working with NVIDIA CUDA GPUs using Julia. It features a user-friendly array abstraction, a compiler for writing CUDA kernels in Julia, and wrappers for various CUDA libraries.
XLA.jl is a package for compiling Julia to XLA for Tensor Processing Unit(TPU).
Nanosoldier.jl is a package for running JuliaCI services on MIT's Nanosoldier cluster.
Julia for VSCode is a powerful extension for the Julia language.
JuMP.jl is a domain-specific modeling language for mathematical optimization embedded in Julia.
Optim.jl is a univariate and multivariate optimization in Julia.
RCall.jl is a package that allows you to call R functions from Julia.
JavaCall.jl is a package that allows you to call Java functions from Julia.
PyCall.jl is a package that allows you to call Python functions from Julia.
MXNet.jl is the Apache MXNet Julia package. MXNet.jl brings flexible and efficient GPU computing and state-of-art deep learning to Julia.
Knet is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. It supports GPU operation and automatic differentiation using dynamic computational graphs for models defined in plain Julia.
Distributions.jl is a Julia package for probability distributions and associated functions.
DataFrames.jl is a tool for working with tabular data in Julia.
Flux.jl is an elegant approach to machine learning. It's a 100% pure-Julia stack, and provides lightweight abstractions on top of Julia's native GPU and AD support.
IRTools.jl is a simple and flexible IR format, expressive enough to work with both lowered and typed Julia code, as well as external IRs.
Cassette.jl is a Julia package that provides a mechanism for dynamically injecting code transformation passes into Julia’s just-in-time (JIT) compilation cycle, enabling post hoc analysis and modification of "Cassette-unaware" Julia programs without requiring manual source annotation or refactoring of the target code.
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