机器学习指南
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机器学习/深度学习框架。
机器学习学习资源
机器学习框架、库和工具
算法
PyTorch 开发
TensorFlow 开发
核心机器学习开发
深度学习开发
强化学习开发
计算机视觉开发
自然语言处理 (NLP) 开发
生物信息学
CUDA开发
MATLAB开发
C/C++开发
Java开发
Python开发
斯卡拉开发
R开发
朱莉娅发展
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机器学习是人工智能 (AI) 的一个分支，专注于使用从数据模型中学习的算法构建应用程序，并随着时间的推移提高其准确性，而无需进行编程。
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Microsoft 自然语言处理 (NLP) 最佳实践
微软自动驾驶手册
Azure 机器学习 - ML 即服务 |微软Azure
如何在 Azure 机器学习工作区中运行 Jupyter Notebook
机器学习和人工智能|亚马逊网络服务
在 Amazon SageMaker 临时实例上安排 Jupyter 笔记本
人工智能与机器学习 |谷歌云
在 Google Cloud 上将 Jupyter Notebook 与 Apache Spark 结合使用
机器学习 |苹果开发者
人工智能与自动驾驶 |特斯拉
元人工智能工具 | Facebook
PyTorch 教程
TensorFlow 教程
Jupyter实验室
Apple Silicon 上的 Core ML 稳定扩散
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斯坦福大学的机器学习作者：Andrew Ng | Coursera
AWS 机器学习 (ML) 课程培训和认证
Microsoft Azure 机器学习奖学金计划 |优达学城
Microsoft 认证：Azure 数据科学家助理
微软认证：Azure AI 工程师助理
Azure 机器学习培训和部署
通过 Google Cloud Training 学习机器学习和人工智能
Google Cloud 机器学习速成课程
在线机器学习课程 |乌德米
在线机器学习课程 | Coursera
通过在线课程学习机器学习 | edX
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机器学习简介 (PDF)
人工智能：一种现代方法 作者：Stuart J. Russel 和 Peter Norvig
深度学习 作者：Ian Goodfellow、Yoshoua Bengio 和 Aaron Courville
Andriy Burkov 的百页机器学习书
机器学习，作者：Tom M. Mitchell
编程集体智慧：构建智能 Web 2.0 应用程序，作者：Toby Segaran
机器学习：算法视角，第二版
模式识别和机器学习 作者：Christopher M. Bishop
使用 Python 进行自然语言处理 作者：Steven Bird、Ewan Klein 和 Edward Loper
Python 机器学习：面向初学者的机器学习技术方法作者：Leonard Eddison
贝叶斯推理和机器学习作者：David Barber
面向绝对初学者的机器学习：奥利弗·西奥博尔德 (Oliver Theobald) 的简单英语介绍
机器学习的实际应用作者：Ben Wilson
使用 Scikit-Learn、Keras 和 TensorFlow 进行机器学习实践：构建智能系统的概念、工具和技术 作者：Aurélien Géron
Python 机器学习简介：数据科学家指南 作者：Andreas C. Müller 和 Sarah Guido
黑客机器学习：帮助您入门的案例研究和算法 作者：Drew Conway 和 John Myles White
《统计学习的要素：数据挖掘、推理和预测》作者：Trevor Hastie、Robert Tibshirani 和 Jerome Friedman
分布式机器学习模式 - 书籍（免费在线阅读）+代码
现实世界的机器学习 [免费章节]
统计学习简介 - 书 + R 代码
统计学习的要素 - 书
Think Bayes - 书籍 + Python 代码
挖掘海量数据集
第一次接触机器学习
机器学习简介 - Alex Smola 和 SVN Vishwanathan
模式识别的概率理论
信息检索简介
预测：原理与实践
机器学习简介 - Amnon Shashua
强化学习
机器学习
对人工智能的探索
数据科学 R 编程
数据挖掘 - 实用的机器学习工具和技术
使用 TensorFlow 进行机器学习
机器学习系统
机器学习基础 - Mehryar Mohri、Afshin Rostamizadeh 和 Ameet Talwalkar
人工智能驱动的搜索 - Trey Grainger、Doug Turnbull、Max Irwin -
机器学习的集成方法 - Gautam Kunapuli
机器学习工程实践 - Ben Wilson
隐私保护机器学习 - J. Morris Chang、Di Zhuang、G. Dumindu Samaraweera
自动化机器学习的实际应用 - Qingquan Song、Haifeng Jin 和 Xia Hu
分布式机器学习模式 - Yuan Tang
管理机器学习项目：从设计到部署 - Simon Thompson
因果机器学习 - Robert Ness
贝叶斯优化实践 - Quan Nguyen
深度机器学习算法）- Vadim Smolyakov
优化算法 - Alaa Khamis
Guillaume Saupin 的实用梯度提升
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TensorFlow 是一个用于机器学习的端到端开源平台。它拥有一个由工具、库和社区资源组成的全面、灵活的生态系统，使研究人员能够推动机器学习领域的最先进技术，并使开发人员能够轻松构建和部署机器学习驱动的应用程序。
Keras 是一种高级神经网络 API，用 Python 编写，能够在 TensorFlow、CNTK 或 Theano 之上运行。它的开发重点是实现快速实验。它能够在 TensorFlow、Microsoft Cognitive Toolkit、R、Theano 或 PlaidML 上运行。
PyTorch 是一个用于对不规则输入数据（例如图形、点云和流形）进行深度学习的库。主要由 Facebook 的人工智能研究实验室开发。
Amazon SageMaker 是一项完全托管的服务，让每位开发人员和数据科学家能够快速构建、训练和部署机器学习 (ML) 模型。 SageMaker 消除了机器学习过程每个步骤的繁重工作，使开发高质量模型变得更加容易。
Azure Databricks 是一项基于 Apache Spark 的快速协作大数据分析服务，专为数据科学和数据工程而设计。 Azure Databricks 可在几分钟内设置 Apache Spark 环境、自动缩放并在交互式工作区中协作处理共享项目。 Azure Databricks 支持 Python、Scala、R、Java 和 SQL，以及数据科学框架和库，包括 TensorFlow、PyTorch 和 scikit-learn。
Microsoft Cognitive Toolkit (CNTK) 是一个用于商业级分布式深度学习的开源工具包。它将神经网络描述为通过有向图的一系列计算步骤。 CNTK 允许用户轻松实现和组合流行的模型类型，例如前馈 DNN、卷积神经网络 (CNN) 和循环神经网络 (RNN/LSTM)。 CNTK 通过跨多个 GPU 和服务器的自动微分和并行化实现随机梯度下降（SGD，误差反向传播）学习。
Apple CoreML 是一个有助于将机器学习模型集成到您的应用程序中的框架。 Core ML 为所有模型提供统一的表示。您的应用程序使用 Core ML API 和用户数据来进行预测以及训练或微调模型，所有这些都在用户的设备上进行。模型是将机器学习算法应用于一组训练数据的结果。您使用模型根据新的输入数据进行预测。
Apache OpenNLP 是一个开源库，用于基于机器学习的工具包，用于处理自然语言文本。它具有适用于命名实体识别、句子检测、POS（词性）标记、标记化特征提取、分块、解析和共指解析等用例的 API。
Apache Airflow 是一个由社区创建的开源工作流管理平台，用于以编程方式编写、安排和监控工作流。安装。原则。可扩展。 Airflow 具有模块化架构，并使用消息队列来编排任意数量的工作人员。气流已准备好扩展到无穷大。
开放神经网络交换 (ONNX) 是一个开放的生态系统，使人工智能开发人员能够随着项目的发展选择正确的工具。 ONNX 为人工智能模型（深度学习和传统机器学习）提供开源格式。它定义了可扩展的计算图模型，以及内置运算符和标准数据类型的定义。
Apache MXNet 是一个专为提高效率和灵活性而设计的深度学习框架。它允许您混合符号和命令式编程，以最大限度地提高效率和生产力。 MXNet 的核心包含一个动态依赖调度程序，可以动态自动并行化符号操作和命令操作。其之上的图形优化层使符号执行快速且内存高效。 MXNet 便携且轻量级，可有效扩展到多个 GPU 和多台机器。支持 Python、R、Julia、Scala、Go、Javascript 等。
AutoGluon 是深度学习工具包，可自动执行机器学习任务，使您能够在应用程序中轻松实现强大的预测性能。只需几行代码，您就可以在表格、图像和文本数据上训练和部署高精度深度学习模型。
Anaconda 是一个非常流行的机器学习和深度学习数据科学平台，使用户能够开发模型、训练和部署模型。
PlaidML 是一种先进的便携式张量编译器，可在笔记本电脑、嵌入式设备或其他可用计算硬件未得到很好支持或可用软件堆栈包含令人不快的许可限制的设备上实现深度学习。
OpenCV 是一个高度优化的库，专注于实时计算机视觉应用。 C++、Python 和 Java 接口支持 Linux、MacOS、Windows、iOS 和 Android。
Scikit-Learn 是一个基于 SciPy、NumPy 和 matplotlib 构建的用于机器学习的 Python 模块，可以更轻松地应用许多流行机器学习算法的强大而简单的实现。
Weka 是一款开源机器学习软件，可以通过图形用户界面、标准终端应用程序或 Java API 进行访问。它广泛用于教学、研究和工业应用，包含大量用于标准机器学习任务的内置工具，并且还提供对 scikit-learn、R 和 Deeplearning4j 等知名工具箱的透明访问。
Caffe 是一个深度学习框架，考虑到了表达、速度和模块化。它由伯克利人工智能研究中心 (BAIR)/伯克利视觉与学习中心 (BVLC) 和社区贡献者开发。
Theano 是一个 Python 库，可让您有效地定义、优化和评估涉及多维数组的数学表达式，包括与 NumPy 的紧密集成。
nGraph 是一个用于深度学习的开源 C++ 库、编译器和运行时。 nGraph 编译器旨在使用任何深度学习框架加速开发 AI 工作负载并部署到各种硬件目标。它为 AI 开发人员提供自由、性能和易用性。
NVIDIA cuDNN 是一个 GPU 加速的深度神经网络基元库。 cuDNN 为标准例程（例如前向和后向卷积、池化、归一化和激活层）提供高度调整的实现。 cuDNN 可加速广泛使用的深度学习框架，包括 Caffe2、Chainer、Keras、MATLAB、MxNet、PyTorch 和 TensorFlow。
Huginn 是一个自托管系统，用于构建代理，为您在线执行自动化任务。它可以读取网络、监视事件并代表您采取操作。 Huginn 的代理创建并使用事件，并沿着有向图传播它们。将其视为您自己的服务器上的 IFTTT 或 Zapier 的可破解版本。
Netron 是神经网络、深度学习和机器学习模型的查看器。它支持 ONNX、TensorFlow Lite、Caffe、Keras、Darknet、PaddlePaddle、ncnn、MNN、Core ML、RKNN、MXNet、MindSpore Lite、TNN、Barracuda、Tengine、CNTK、TensorFlow.js、Caffe2 和 UFF。
多巴胺是一个用于强化学习算法快速原型设计的研究框架。
DALI 是一个 GPU 加速库，包含高度优化的构建块和用于数据处理的执行引擎，以加速深度学习训练和推理应用程序。
MindSpore Lite 是一个新的开源深度学习训练/推理框架，可用于移动、边缘和云场景。
Darknet 是一个用 C 和 CUDA 编写的开源神经网络框架。它速度快、易于安装，并支持 CPU 和 GPU 计算。
PaddlePaddle是一个易用、高效、灵活、可扩展的深度学习平台，最初由百度科学家和工程师开发，旨在将深度学习应用到百度的众多产品中。
GoogleNotebookLM 是一种实验性 AI 工具，利用语言模型的强大功能与您现有的内容相结合，更快地获得关键见解。类似于虚拟研究助理，可以总结事实，解释复杂的想法，并根据您选择的来源集思广益新的联系。
Unilm 是一种跨任务、语言和模式的大规模自我监督预训练。
语义内核 (SK) 是一种轻量级 SDK，可将 AI 大语言模型 (LLM) 与传统编程语言集成。 SK 可扩展编程模型结合了自然语言语义功能、传统代码本机功能和基于嵌入的内存，释放了新的潜力，并为人工智能应用程序增加了价值。
Pandas AI 是一个 Python 库，它将生成人工智能功能集成到 Pandas 中，使数据帧具有对话性。
NCNN 是针对移动平台优化的高性能神经网络推理框架。
MNN 是一个极快的轻量级深度学习框架，经过阿里巴巴关键业务用例的实际测试。
MediaPipe 针对多种平台上的端到端性能进行了优化。查看演示 了解更多 复杂的设备上 ML，已简化 我们已经抽象化了使设备上 ML 可定制、可用于生产且可跨平台访问的复杂性。
MegEngine 是一个快速、可扩展且用户友好的深度学习框架，具有 3 个关键功能： 用于训练和推理的统一框架。
ML.NET 是一个机器学习库，被设计为可扩展平台，以便您可以使用其他流行的 ML 框架（TensorFlow、ONNX、Infer.NET 等）并访问更多机器学习场景，例如图像分类、物体检测等等。
Ludwig 是一个声明性机器学习框架，可以使用简单而灵活的数据驱动配置系统轻松定义机器学习管道。
MMdnn 是一款全面的跨框架工具，用于转换、可视化和诊断深度学习 (DL) 模型。 “MM”代表模型管理，“dnn”是深度神经网络的缩写。在 Caffe、Keras、MXNet、Tensorflow、CNTK、PyTorch Onnx 和 CoreML 之间转换模型。
Horovod 是一个适用于 TensorFlow、Keras、PyTorch 和 Apache MXNet 的分布式深度学习训练框架。
Vaex 是一个高性能 Python 库，用于惰性外核数据帧（类似于 Pandas），用于可视化和探索大型表格数据集。
GluonTS 是一个用于概率时间序列建模的 Python 包，专注于基于深度学习的模型，基于 PyTorch 和 MXNet。
MindsDB 是一个 ML-SQL Server，支持使用 SQL 为最强大的数据库和数据仓库提供机器学习工作流程。
Jupyter Notebook 是一款开源 Web 应用程序，可让您创建和共享包含实时代码、方程、可视化和叙述文本的文档。 Jupyter 广泛应用于数据清理和转换、数值模拟、统计建模、数据可视化、数据科学和机器学习等行业。
Apache Spark 是用于大规模数据处理的统一分析引擎。它提供了 Scala、Java、Python 和 R 中的高级 API，以及支持用于数据分析的通用计算图的优化引擎。它还支持一组丰富的高级工具，包括用于 SQL 和 DataFrames 的 Spark SQL、用于机器学习的 MLlib、用于图形处理的 GraphX 以及用于流处理的 Structured Streaming。
适用于 SQL Server 和 Azure SQL 的 Apache Spark 连接器是一种高性能连接器，使您能够在大数据分析中使用事务数据，并保留临时查询或报告的结果。该连接器允许您使用本地或云中的任何 SQL 数据库作为 Spark 作业的输入数据源或输出数据接收器。
Apache PredictionIO 是一个面向开发人员、数据科学家和最终用户的开源机器学习框架。它支持事件收集、算法部署、评估、通过 REST API 查询预测结果。它基于 Hadoop、HBase（和其他数据库）、Elasticsearch、Spark 等可扩展的开源服务，并实现了所谓的 Lambda 架构。
Cluster Manager for Apache Kafka（CMAK）是一个用于管理 Apache Kafka 集群的工具。
BigDL 是 Apache Spark 的分布式深度学习库。借助 BigDL，用户可以将深度学习应用程序编写为标准 Spark 程序，这些程序可以直接在现有 Spark 或 Hadoop 集群之上运行。
Eclipse Deeplearning4J (DL4J) 是一组项目，旨在支持基于 JVM（Scala、Kotlin、Clojure 和 Groovy）深度学习应用程序的所有需求。这意味着从原始数据开始，从任何地方、任何格式加载和预处理它，以构建和调整各种简单和复杂的深度学习网络。
Tensorman 是由 System76 开发的一个用于轻松管理 Tensorflow 容器的实用程序。Tensorman 允许 Tensorflow 在与系统其余部分隔离的隔离环境中运行。该虚拟环境可以独立于基本系统运行，允许您在支持 Docker 运行时的任何版本的 Linux 发行版上使用任何版本的 Tensorflow。
Numba 是一个开源的、支持 NumPy 的 Python 优化编译器，由 Anaconda, Inc. 赞助。它使用 LLVM 编译器项目从 Python 语法生成机器代码。 Numba 可以编译大量以数字为中心的 Python 子集，包括许多 NumPy 函数。此外，Numba 支持循环的自动并行化、GPU 加速代码的生成以及 ufunc 和 C 回调的创建。
Chainer 是一个基于 Python 的深度学习框架，旨在实现灵活性。它提供基于运行定义方法（动态计算图）的自动微分 API 以及面向对象的高级 API 来构建和训练神经网络。它还支持使用 CuPy 的 CUDA/cuDNN 进行高性能训练和推理。
XGBoost 是一个优化的分布式梯度提升库，旨在高效、灵活和便携。它在 Gradient Boosting 框架下实现机器学习算法。 XGBoost 提供了并行树提升（也称为 GBDT、GBM），可以快速准确地解决许多数据科学问题。它支持多台机器上的分布式训练，包括AWS、GCE、Azure和Yarn集群。它还可以与Flink、Spark等云数据流系统集成。
cuML 是一套库，用于实现机器学习算法和数学原语函数，与其他 RAPIDS 项目共享兼容的 API。 cuML 使数据科学家、研究人员和软件工程师能够在 GPU 上运行传统的表格 ML 任务，而无需深入了解 CUDA 编程的细节。在大多数情况下，cuML 的 Python API 与 scikit-learn 的 API 匹配。
Emu 是 Rust 的 GPGPU 库，重点关注可移植性、模块化和性能。它是基于 WebGPU 的 CUDA 式计算特定抽象，提供特定功能以使 WebGPU 感觉更像 CUDA。
Scalene 是一个适用于 Python 的高性能 CPU、GPU 和内存分析器，它可以完成许多其他 Python 分析器没有也不能做的事情。它的运行速度比许多其他分析器快几个数量级，同时提供更详细的信息。
MLpack 是一个用 C++ 编写的快速、灵活的 C++ 机器学习库，构建在 Armadillo 线性代数库、ensmallen 数值优化库和 Boost 的部分基础上。
Netron 是神经网络、深度学习和机器学习模型的查看器。它支持 ONNX、TensorFlow Lite、Caffe、Keras、Darknet、PaddlePaddle、ncnn、MNN、Core ML、RKNN、MXNet、MindSpore Lite、TNN、Barracuda、Tengine、CNTK、TensorFlow.js、Caffe2 和 UFF。
Lightning 是一种构建和训练 PyTorch 模型并使用 Lightning 应用程序模板将它们连接到 ML 生命周期的工具，无需处理 DIY 基础设施、成本管理、扩展等。
OpenNN 是一个用于机器学习的开源神经网络库。它包含复杂的算法和实用程序来处理许多人工智能解决方案。
H20 是一个人工智能云平台，可解决复杂的业务问题并加速新想法的发现，并提供您可以理解和信任的结果。
Gensim 是一个用于主题建模、文档索引和大型语料库相似性检索的 Python 库。目标受众是自然语言处理 (NLP) 和信息检索 (IR) 社区。
llama.cpp 是 Facebook 的 LLaMA 模型的 C/C++ 端口。
hmmlearn 是一组用于隐马尔可夫模型的无监督学习和推理的算法。
Nextjournal 是一个用于可重复研究的笔记本。它运行您可以放入 Docker 容器中的任何内容。通过多语言笔记本、自动版本控制和实时协作改进您的工作流程。通过按需配置（包括 GPU 支持）节省时间和金钱。
IPython 为交互式计算提供了丰富的架构：
Veles 是三星目前开发的一个用于快速深度学习应用程序开发的分布式平台。
DyNet 是由卡内基梅隆大学和其他许多大学开发的神经网络库。它是用 C++ 编写的（在 Python 中绑定），旨在在 CPU 或 GPU 上运行时高效，并且能够与具有针对每个训练实例而变化的动态结构的网络良好地配合。这类网络在自然语言处理任务中尤其重要，DyNet 已被用来构建用于句法解析、机器翻译、形态变化和许多其他应用领域的最先进的系统。
Ray 是用于扩展 AI 和 Python 应用程序的统一框架。它由核心分布式运行时和用于加速 ML 工作负载的库工具包 (Ray AIR) 组成。
Whisper.cpp 是 OpenAI 的 Whisper 自动语音识别 (ASR) 模型的高性能推理。
ChatGPT Plus 是 ChatGPT 的试点订阅计划（ 20 美元/月），ChatGPT 是一种对话式 AI，可以与您聊天、回答后续问题并挑战不正确的假设。
Auto-GPT 是一种“人工智能代理”，它以自然语言给出目标，可以尝试通过将其分解为子任务并在自动循环中使用互联网和其他工具来实现它。它使用 OpenAI 的 GPT-4 或 GPT-3.5 API，是使用 GPT-4 执行自主任务的应用程序的首批示例之一。
mckaywrigley 开发的 Chatbot UI 是一款适用于 OpenAI 聊天模型的高级聊天机器人套件，使用 Next.js、TypeScript 和 Tailwind CSS 在 Chatbot UI Lite 之上构建。此版本的 ChatBot UI 支持 GPT-3.5 和 GPT-4 模型。对话存储在您的浏览器本地。您可以导出和导入对话以防止数据丢失。查看演示。
mckaywrigley 开发的 Chatbot UI Lite 是一个简单的聊天机器人入门套件，适用于使用 Next.js、TypeScript 和 Tailwind CSS 的 OpenAI 聊天模型。查看演示。
MiniGPT-4 是一种通过高级大语言模型增强视觉语言理解的方法。
GPT4All 是一个开源聊天机器人生态系统，经过大量干净助理数据的训练，包括基于 LLaMa 的代码、故事和对话。
GPT4All UI 是一个 Flask Web 应用程序，提供用于与 GPT4All 聊天机器人交互的聊天 UI。
Alpaca.cpp 是您设备上本地的类似 ChatGPT 的快速模型。它将 LLaMA 基础模型与斯坦福羊驼的开放复制相结合，对基础模型进行微调以服从指令（类似于用于训练 ChatGPT 的 RLHF），并对 llama.cpp 进行了一系列修改以添加聊天界面。
llama.cpp 是 Facebook 的 LLaMA 模型的 C/C++ 端口。
OpenPlayground 是一个在您的设备上本地运行类似 ChatGPT 模型的游乐场。
Vicuna 是一个经过微调 LLaMA 训练的开源聊天机器人。它显然达到了 chatgpt 90% 以上的质量，并且训练成本为 300 美元。
Yeagar ai 是一款 Langchain 代理创建器，旨在帮助您轻松构建、原型设计和部署人工智能驱动的代理。
Vicuna 是通过使用从 ShareGPT.com 和公共 API 收集的大约 7 万个用户共享对话对 LLaMA 基本模型进行微调而创建的。为了确保数据质量，它将 HTML 转换回 Markdown，并过滤掉一些不合适或低质量的样本。
ShareGPT 是一个一键分享您最疯狂的 ChatGPT 对话的地方。截至目前，共有 198,404 条对话被分享。
FastChat 是一个开放平台，用于训练、服务和评估基于大型语言模型的聊天机器人。
Haystack 是一个开源 NLP 框架，可使用 Transformer 模型和 LLM（GPT-4、ChatGPT 等）与数据进行交互。它提供生产就绪的工具来快速构建复杂的决策、问答、语义搜索、文本生成应用程序等。
StableLM（Stability AI Language Models）是StableLM系列语言模型，并将不断更新新的检查点。
Databricks 的 Dolly 是一种遵循指令的大型语言模型，在 Databricks 机器学习平台上进行训练，并获得商业用途许可。
GPTCach 是一个用于为 LLM 查询创建语义缓存的库。
AlaC 是一个人工智能基础设施即代码生成器。
Adrenaline 是一个可让您与代码库对话的工具。它由静态分析、向量搜索和大型语言模型提供支持。
OpenAssistant 是一个基于聊天的助手，它可以理解任务，可以与第三方系统交互，并动态检索信息来执行此操作。
DoctorGPT 是一个轻量级的独立二进制文件，可以监视应用程序日志中的问题并进行诊断。
HttpGPT 是一个虚幻引擎 5 插件，可通过异步 REST 请求促进与 OpenAI 基于 GPT 的服务（ChatGPT 和 DALL-E）的集成，使开发人员可以轻松地与这些服务进行通信。它还包括编辑器工具，可将 Chat GPT 和 DALL-E 图像生成直接集成到引擎中。
PaLM 2 是下一代大型语言模型，建立在 Google 在机器学习和负责任的 AI 领域突破性研究的基础上。它包括高级推理任务，包括代码和数学、分类和问答、翻译和多语言能力以及自然语言生成，比我们以前最先进的法学硕士更好。
Med-PaLM 是一个大型语言模型 (LLM)，旨在为医学问题提供高质量的答案。它利用了 Google 大型语言模型的强大功能，我们通过一系列精心策划的医学专家演示将其与医学领域结合起来。
Sec-PaLM 是一种大型语言模型 (LLM)，可加快帮助负责维护组织安全的人员的能力。这些新模型不仅为人们提供了一种更自然、更有创意的方式来理解和管理安全。
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LocalAI 是一个自托管、社区驱动、本地 OpenAI 兼容的 API。在消费级硬件上运行 LLM 的 OpenAI 的直接替代品，无需 GPU。它是一个运行 ggml 兼容模型的 API：llama、gpt4all、rwkv、whisper、vicuna、koala、gpt4all-j、cerebras、falcon、dolly、starcoder 等。
llama.cpp 是 Facebook 的 LLaMA 模型的 C/C++ 端口。
ollama 是一个在本地启动并运行 Llama 2 和其他大型语言模型的工具。
LocalAI 是一个自托管、社区驱动、本地 OpenAI 兼容的 API。在消费级硬件上运行 LLM 的 OpenAI 的直接替代品，无需 GPU。它是一个运行 ggml 兼容模型的 API：llama、gpt4all、rwkv、whisper、vicuna、koala、gpt4all-j、cerebras、falcon、dolly、starcoder 等。
Serge 是一个通过 llama.cpp 与 Alpaca 聊天的 Web 界面。完全自托管和 Docker 化，具有易于使用的 API。
OpenLLM 是一个用于在生产中操作大型语言模型 (LLM) 的开放平台。轻松微调、服务、部署和监控任何法学硕士。
Llama-gpt 是一个自托管、离线、类似 ChatGPT 的聊天机器人。由 Llama 2 提供支持。100% 私密，不会有任何数据离开您的设备。
Llama2 webui 是一个工具，可以从任何地方（Linux/Windows/Mac）在 GPU 或 CPU 上使用 gradio UI 本地运行任何 Llama 2。使用llama2-wrapper作为生成代理/应用程序的本地 llama2 后端。
llama2.c是一种训练Pytorch中Llama 2 LLM体系结构的工具，然后用一个简单的700线C文件（Run.C）推进它。
羊驼毛。它结合了Llama Foundation模型与Stanford羊驼的公开再现，对基本模型进行微调以遵守说明（类似于用于培训ChatGpt的RLHF），并对Llama.cpp进行了一组修改以添加聊天界面。
GPT4All是一个由开源聊天机器人组成的生态系统，该生态系统培训了大量的清洁助理数据，包括基于Llama的代码，故事和对话。
Minigpt-4是具有高级大语言模型的增强视觉理解
Lollms WebUI是LLM（大语言模型）模型的枢纽。它旨在提供一个用户友好的界面，以访问和利用各种LLM型号进行各种任务。无论您是在写作，编码，组织数据，生成图像还是寻求问题的答案方面需要帮助。
LM Studio是发现，下载和运行本地LLM的工具。
Gradio Web UI是大型语言模型的工具。支持Transformers，GPTQ，Llama.cpp（GGML/gguf），Llama型号。
OpenPlayground是一个游戏范围，用于在设备上本地运行类似Chatgpt的模型。
Vicuna是由通过微调美洲驼训练的开源聊天机器人。显然，它达到了90％以上的CHATGPT，训练费用为300美元。
Yeagar AI是一种旨在帮助您轻松构建，原型和部署AI驱动的代理商的兰班代理创建者。
KoboldCPP是用于GGML型号的易于使用的AI文本生成软件。这是一个可以从consedo中分发的单个自我包含的，它可以在llama.cpp上构建，并添加了一种多功能的kobold api端点，额外的格式支持，向后兼容性以及具有持久故事，编辑工具，保存格式，内存，世界，世界，世界，世界，世界，世界的编辑工具的花式UI信息，作者注，字符和方案。
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模糊逻辑是一种启发式方法，它允许更高级的决策树处理并与基于规则的编程更好地集成。
模糊逻辑系统的体系结构。资料来源：研究门
支持向量机（SVM）是一种监督的机器学习模型，它使用分类算法来解决两组分类问题。
支持向量机（SVM）。来源：OpenClipart
神经网络是机器学习的子集，是深度学习算法的核心。名称/结构的灵感来自于人脑将生物神经元/节点互相信号的过程复制到彼此的过程中。
深神经网络。资料来源：IBM
卷积神经网络（R-CNN）是一种对象检测算法，该算法首先将图像段以找到潜在的相关边界框，然后运行检测算法以在这些边界框中找到最可能的对象。
卷积神经网络。来源：CS231N
复发性神经网络（RNN）是一种使用顺序数据或时间序列数据的人工神经网络。
复发性神经网络。来源：SlideTeam
多层感知器（MLP）是多层神经网络，由具有阈值激活的多层感知器组成。
多层感知。资料来源：Deepai
Random Forest是一种常用的机器学习算法，它结合了多个决策树的输出以达到单个结果。森林中的决策树不能修剪用于抽样，因此可以选择预测。当它处理分类和回归问题时，其易于使用和灵活性促进了其采用。
随机森林。资料来源：Wikimedia
决策树是用于分类和回归的树结构模型。
**决策树。资料来源：CMU
幼稚的贝叶斯是一种机器学习算法，用于解决CALSSIFIESION问题。它基于在功能之间应用具有强烈独立性假设的贝叶斯定理。
贝叶斯定理。资料来源：Mathisfun
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Pytorch是一个开源深度学习框架，可加速从研究到生产的道路，用于计算机视觉和自然语言处理等应用。 Pytorch由Facebook的AI研究实验室开发。
pytorch入门
Pytorch文档
Pytorch讨论论坛
顶级Pytorch课程在线| Coursera
顶级Pytorch课程在线|乌德米
通过在线课程和课程学习Pytorch | edX
Pytorch基本面 - 学习|微软文档
pytorch深入学习的介绍|优达学城
视觉工作室代码中的Pytorch开发
azure上的pytorch-深度学习与pytorch |微软Azure
Pytorch -Azure Databricks |微软文档
与Pytorch深入学习|亚马逊网络服务 (AWS)
Google Cloud上的Pytorch入门
Pytorch Mobile是从培训到iOS和Android移动设备部署的端到端ML工作流程。
Torchscript是从Pytorch代码中创建可序列化且可优化的模型的一种方式。这允许将任何火有关程序从python过程中保存，并加载在没有python依赖性的过程中。
Torchserve是一种灵活且易于使用的工具，用于提供Pytorch型号。
Keras是一种高级神经网络API，用Python编写，能够在Tensorflow，CNTK或Theano的顶部运行。它是开发出来的，重点是实现快速实验。它能够在Tensorflow，Microsoft认知工具包，R，Theano或Plaidml上运行。
ONX运行时是跨平台，高性能ML推断和训练加速器。它支持来自Pytorch和Tensorflow/keras等深度学习框架的模型，以及Scikit-Leartn，LightGBM，XGBoost等古典机器学习库，等等。
Kornia是一个可区分的计算机视觉库，由一组例程和可区分模块组成，以解决通用简历（计算机视觉）问题。
Pytorch-NLP是Python中自然语言处理（NLP）的库。它是考虑到最新研究的基础，从第一天开始设计以支持快速原型制作。 Pytorch-NLP带有预训练的嵌入式，采样器，数据集加载程序，指标，神经网络模块和文本编码器。
Ignite是一个高级库，可帮助训练和评估Pytorch中的神经网络，并透明地透明。
Hummingbird是一个库，用于将经过训练的传统ML模型汇编成张量计算。它允许用户无缝利用神经网络框架（例如Pytorch）加速传统的ML模型。
Deep Graph Library（DGL）是一个python软件包，可在Pytorch和其他框架之上易于实现图形神经网络模型家族。
Tensorly是Python中张量方法和深度张力神经网络的高级API，旨在使张量学习变得简单。
GpyTorch是一个使用Pytorch实施的高斯过程库，旨在创建可扩展的灵活高斯流程模型。
Poutyne是一个类似于皮塔尔（Keras）的框架，可用于训练神经网络所需的许多清单代码。
Forte是用于构建具有可组合组件，方便的数据接口和交叉任务交互的NLP管道的工具包。
Torchmetrics是用于分布式可扩展Pytorch应用程序的机器学习指标。
Captum是开源的，可扩展的库，用于建立在Pytorch上的模型。
变压器是用于Pytorch，Tensorflow和Jax的最先进的自然语言处理。
Hydra是优雅配置复杂应用程序的框架。
加速是一种使用多GPU，TPU，混合精液训练和使用Pytorch型号的简单方法。
Ray是一个快速而简单的框架，用于构建和运行分布式应用程序。
Parlai是一个统一的平台，用于共享，培训和评估许多任务的对话模型。
Pytorchvideo是一个深入学习库，用于视频理解研究。托管各种以视频为中心的模型、数据集、训练管道等。
Opacus是一个图书馆，可培训具有不同隐私的Pytorch模型。
Pytorch Lightning是Pytorch的类似Keras的ML库。它为您留下了核心培训和验证逻辑，并自动化其余的。
pytorch几何颞时间是Pytorch几何的时间（动态）扩展库。
PyTorch Geometric 是一个用于对图形、点云和流形等不规则输入数据进行深度学习的库。
栅格视觉是卫星和空中图像深度学习的开源框架。
Crypten是保留ML隐私的框架。它的目标是使ML从业者可以使用安全的计算技术。
Optuna是一种开源的超参数优化框架，可自动化超参数搜索。
Pyro是用Python编写的通用概率编程语言（PPL），并由Pytorch在后端支持。
标记是一个快速且可扩展的图像增强库，用于不同的CV任务，例如分类，分割，对象检测和姿势估计。
Skorch是一个用于Pytorch的高级库，可提供完整的Scikit-Learn兼容性。
MMF是Facebook AI研究（FAIR）的视觉和语言多模式研究的模块化框架。
ADAPTDL是一种资源自适应的深度学习培训和调度框架。
Polyaxon是建造，培训和监视大规模深度学习应用程序的平台。
Textbrewer是一种基于Pytorch的知识蒸馏工具包，用于自然语言处理
Advertorch是用于对抗性鲁棒性研究的工具箱。它包含用于生成对抗性示例和防御攻击的模块。
Nemo是对话型AI的AA工具包。
ClinicAdl是对阿尔茨海默氏病再现分类的框架
稳定的基线3（SB3）是Pytorch中强化学习算法的可靠实现。
Torchio是一组工具，可以在Pytorch编写的深度学习应用程序中有效阅读，预处理，样本，增强和编写3D医学图像。
Pysyft是一个Python图书馆，用于保存深度学习的隐私。
Flair是最先进的自然语言处理（NLP）的非常简单的框架。
Glow是ML编译器，可在不同的硬件平台上加速深度学习框架的性能。
FairScale是一个用于高性能的Pytorch扩展库和一台或多台机器/节点上的大规模培训。
MONAI是一个深度学习框架，可为开发医疗成像培训工作流程提供域优化的基础能力。
PFRL是一个深厚的增强学习库，它使用Pytorch实现了Python中各种最新的深化算法。
EINOPS是一种灵活而强大的张量操作，用于可读和可靠的代码。
Pytorch3d是一个深度学习库，可为Pytorch提供有效的，可重复使用的组件，用于3D计算机视觉研究。
Ensemble Pytorch是Pytorch提高深度学习模型的性能和鲁棒性的统一合奏框架。
Lightly是用于自学学习的计算机视觉框架。
更高的是一个库，它促进了使用近vanilla pytorch的任意复杂基于梯度的元学习算法和嵌套优化环的实现。
Horovod是一个用于深度学习框架的分布式培训库。 Horovod旨在使分布式DL快速易于使用。
Pennylane是用于量子ML，自动分化和优化杂种量子古典计算的库。
检测2是Fair的下一代平台，用于对象检测和分割。
Fastai是一个图书馆，可以使用现代最佳实践简化训练快速准确的神经网。
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TensorFlow是机器学习的端到端开源平台。它拥有一个全面，灵活的工具，图书馆和社区资源的生态系统，使研究人员可以推动ML的最新技术，开发人员可以轻松构建和部署ML供电的应用程序。
开始使用TensorFlow
TensorFlow 教程
Tensorflow开发人员证书| TensorFlow
Tensorflow社区
TensorFlow模型和数据集
TensorFlow云
机器学习教育| TensorFlow
在线顶级张量流课程| Coursera
在线顶级张量流课程|乌德米
用TensorFlow进行深度学习|乌德米
用TensorFlow进行深度学习| edX
深度学习的TensorFlow介绍|优达学城
TensorFlow的介绍：机器学习速效课程| Google开发人员
训练和部署张量流型号 - Azure机器学习
用Python和TensorFlow将机器学习模型应用于Azure功能|微软Azure
用TensorFlow进行深度学习|亚马逊网络服务 (AWS)
TensorFlow -Amazon EMR | AWS 文档
Tensorflow Enterprise |谷歌云
Tensorflow Lite是用于在移动设备和IoT设备上部署机器学习模型的开源深度学习框架。
tensorflow.js是一个JavaScript库，可让您在JavaScript中开发或执行ML模型，并直接在浏览器客户端，通过Node.js在服务器端，通过react react Native，通过Electron甚至IOT上的Mobile Native使用服务器端，通过raspberry pi上的node.js设备。
Tensorflow_macos是使用Apple的ML Compute Framework加速的MACOS 11.0+的TensorFlow和TensorFlow插件的MAC优化版本。
Google Colagoratory是一个免费的Jupyter笔记本电脑环境，不需要设置并完全在云中运行，从而使您可以单击一键在浏览器中执行TensorFlow代码。
假设工具是用于机器学习模型的无代码探测工具，可用于模型理解，调试和公平性。可在张板和jupyter或Colab笔记本电脑中找到。
Tensorboard是一套可视化工具的套件，可以理解，调试和优化TensorFlow程序。
Keras是一种高级神经网络API，用Python编写，能够在Tensorflow，CNTK或Theano的顶部运行。它是开发出来的，重点是实现快速实验。它能够在Tensorflow，Microsoft认知工具包，R，Theano或Plaidml上运行。
XLA（加速线性代数）是线性代数的域特异性编译器，可优化张量计算。结果是服务器和移动平台上的速度，内存使用情况以及可移植性的改进。
ML Perf是一个广泛的ML基准套件，用于测量ML软件框架，ML硬件加速器和ML云平台的性能。
Tensorflow游乐场是一个开发环境，可以在浏览器中使用神经网络修补。
TPU Research Cloud（TRC）是一个程序，使研究人员无需申请访问1000多个云TPU的群集，以帮助他们加速下一波研究突破。
MLIR是一种新的中间表示和编译器框架。
晶格是一个具有常识性形状约束的灵活，控制和可解释的ML解决方案的库。
TensorFlow Hub是可重复使用的机器学习的库。以最少的代码下载并重复使用最新训练的型号。
Tensorflow Cloud是一个将您的本地环境连接到Google Cloud的库。
Tensorflow模型优化工具包是一套用于优化部署和执行的ML模型的工具。
TensorFlow推荐器是用于构建建议系统模型的库。
TensorFlow文本是与TensorFlow 2一起使用的文本和NLP相关类和OPS的集合。
TensorFlow Graphics是计算机图形功能的库，范围从相机，灯光和材料到渲染器。
Tensorflow联合是用于机器学习和其他分散数据计算的开源框架。
TensorFlow概率是用于概率推理和统计分析的库。
Tensor2Tensor是一个深度学习模型和数据集的库，旨在使深度学习更加易于访问和加速ML研究。
TensorFlow隐私是一个Python库，其中包括用于具有不同隐私的训练机器学习模型的张量优化器的实现。
TensorFlow排名是在TensorFlow平台上进行学习级（LTR）技术的库。
TensorFlow代理是用于张力流中加固学习的库。
TensorFlow addons是一个符合公认的API模式的贡献存储库，但是实现由SIG插件维护的核心Tensorflow中不可用的新功能。 TensorFlow 本身支持大量运算符、层、指标、损失和优化器。
TensorFlow I/O是由SIG IO维护的数据集，流和文件系统扩展。
Tensorflow量子是一个量子机学习库，用于快速原型化杂交量子古典ML模型。
多巴胺是一个用于强化学习算法快速原型设计的研究框架。
TRFL是一个由DeepMind创建的增强学习构建块的库。
网格张量集是一种用于分布深度学习的语言，能够指定一类广泛的分布式张量计算。
破烂的tensors是一种API，可以易于存储和操纵具有不均匀形状的数据，包括文本（单词，句子，字符）和可变长度的批次。
Unicode OPS是一种API，它支持直接在Tensorflow中使用Unicode文本。
Magenta是一个研究项目，探讨了机器学习在创作艺术和音乐过程中的作用。
Nucleus是Python和C ++代码的库，旨在使易于读取，写入和分析的通用基因组文件格式的数据（如SAM和VCF）。
SONNET是来自DeepMind的图书馆，用于构建神经网络。
神经结构化学习是一个学习框架，除了功能投入外，还通过利用结构化信号来训练神经网络。
模型补救是一个库，可帮助创建和训练模型，以减少或消除由于基本绩效偏见而造成的用户伤害。
公平指标是一个库，可以轻松计算二进制和多类分类器的普遍识别公平度量标准。
决策森林是一种用于培训，服务和解释模型的最先进算法，这些模型使用决策森林进行分类，回归和排名。
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Core ML是一个Apple框架，用于将机器学习模型集成到Apple设备（包括iOS，WatchOS，MacOS和TVOS）上的应用程序中。 Core ML引入了公共文件格式（.MLMODEL），以用于一组广泛的ML方法，包括深神经网络（卷积和经常性），带有增强的树团和广义线性模型。该格式的模型可以通过XCode直接集成到应用中。
核心ML简介
将核心ML模型集成到您的应用中
核心ML模型
核心ML API参考
核心ML规范
Apple开发人员论坛的核心ML
顶级核心ML课程在线|乌德米
顶级核心ML课程在线| Coursera
核心ML的IBM Watson服务|国际商业机器公司
使用IBM Maximo视觉检查生成核心ML资产|国际商业机器公司
核心ML工具是一个项目，其中包含用于核心ML模型转换，编辑和验证的支持工具。
Create ML是一种在Mac上培训机器学习模型的新方法。它使模型训练的复杂性在产生强大的核心ML模型的同时，使其变得复杂。
Tensorflow_macos是使用Apple的ML Compute Framework加速的MACOS 11.0+的TensorFlow和TensorFlow插件的MAC优化版本。
Apple Vision是一个框架，可执行面部和面部标志性检测，文本检测，条形码识别，图像注册和一般功能跟踪。视觉还允许使用自定义核心ML模型进行分类或对象检测等任务。
Keras是一种高级神经网络API，用Python编写，能够在Tensorflow，CNTK或Theano的顶部运行。它是开发出来的，重点是实现快速实验。它能够在Tensorflow，Microsoft认知工具包，R，Theano或Plaidml上运行。
XGBoost是一个优化的分布式梯度提升库，旨在高效，灵活和便携。它在梯度提升框架下实现机器学习算法。 XGBOOST提供了平行的树（也称为GBDT，GBM），以快速准确的方式解决许多数据科学问题。它支持多台机器上的分布培训，包括AWS，GCE，Azure和纱线簇。另外，它可以与Flink，Spark和其他Cloud DataFlow系统集成。
LIBSVM是用于支持向量分类的集成软件（C-SVC，NU-SVC），回归（Epsilon-SVR，NU-SVR）和分布估计（一级SVM）。它支持多类分类。
Scikit-Learn是用于数据挖掘和数据分析的简单有效的工具。它建立在Numpy，Scipy和MathPlotlib上。
Xcode包括开发人员为Mac，iPhone，iPad，Apple TV和Apple Watch创建出色应用所需的一切。 Xcode为开发人员提供了一个统一的工作流程，用于用户界面设计，编码，测试和调试。 Xcode是作为通用应用程序构建的，该应用程序在基于Intel的CPU和Apple Silicon上100％运行。它包括一个统一的MACOS SDK，其中包含所有框架，编译器，调试器和其他工具，您需要构建在Apple Silicon和Intel X86_64 CPU上本地运行的应用程序。
SwiftUi是一个用户界面工具包，可为声明应用程序的用户界面提供视图，控件和布局结构。 SwiftUI框架为您的应用程序提供了事件处理程序，可将TAP，手势和其他类型的输入输入。
Uikit是一个框架，为您的iOS或TVOS应用提供了所需的基础架构。它提供了用于实现界面的窗口和查看体系结构，事件处理基础架构，用于将多点触摸和其他类型的输入提供给您的应用程序，以及管理用户，系统和应用程序之间的交互所需的主运行循环。
AppKit是一个图形用户界面工具包，包含您需要实现MacOS应用程序的用户界面（例如Windows，面板，按钮，菜单，滚动器和文本字段）的所有对象，并且可以为您处理所有详细信息，因为它有效地处理了所有详细信息在屏幕上借鉴，与硬件设备和屏幕缓冲区进行通信，在绘图前清除屏幕区域，并剪辑视图。
Arkit是一组软件开发工具集，可使开发人员为Apple开发的iOS构建增强现实应用程序。最新版本Arkit 3.5利用了iPad Pro（2020）上的新的LiDAR扫描仪和深度传感系统来支持新一代的AR应用程序，该应用程序使用场景几何以增强场景理解和对象遮挡。
RealityKit是一个实施高性能3D模拟和渲染的框架，该框架通过ARKIT框架提供的信息将虚拟对象无缝整合到现实世界中。
SceneKit是一个高级3D图形框架，可帮助您在iOS应用中创建3D动画场景和效果。
仪器是Xcode工具集的一部分的强大而灵活的性能分析和测试工具。它旨在帮助您介绍iOS，WatchOS，TVOS和MACOS应用程序，流程和设备，以更好地理解和优化其行为和性能。
Cocoapods是Xcode项目中Swift和Objective-C的依赖管理器，通过在简单的文本文件中指定项目的依赖项。然后，Cocoapods递归解决库之间的依赖关系，为所有依赖关系提供源代码，并创建和维护一个Xcode工作空间来构建您的项目。
AppCode正在不断监视代码的质量。它警告您错误和气味，并建议快速修复以自动解决它们。 AppCode为Objective-C，Swift，C/C ++提供大量代码检查以及其他支持语言的许多代码检查。
回到顶部
深度学习是机器学习的一部分，它本质上是一个具有三个或三个层的神经网络。但是，这些神经网络试图模拟人脑的行为，这与其能力远不匹配。这使神经网络可以从大量数据中“学习”。可以监督，半监督或无监督。
深度学习在线课程|英伟达
顶级学习课程在线| Coursera
顶级学习课程在线|乌德米
通过在线课程和课程学习深入学习| edX
深度学习在线课程Nanodegree |优达学城
Andrew Ng的机器学习课程| Coursera
Andrew Ng的生产机器学习工程（MLOP）课程| Coursera
数据科学：Python的深度学习和神经网络|乌德米
使用Python了解机器学习|多元视野
如何思考机器学习算法|多元视野
深度学习课程|斯坦福在线
深度学习 - UW专业和继续教育
深度学习在线课程|哈佛大学
每个人的机器学习课程|数据营
人工智能专家课程：白金版|乌德米
顶级人工智能课程在线| Coursera
通过在线课程和课程学习人工智能| edX
人工智能计算机科学专业证书| edX
人工智能纳米化程序
人工智能（AI）在线课程|优达学城
人工智能课程的介绍|优达学城
IoT开发人员课程的Edge AI |优达学城
推理：目标树和基于规则的专家系统|麻省理工学院开放课件
专家系统和应用人工智能
自主系统-Microsoft AI
Microsoft Project Bonsai简介
Microsoft自主系统平台的机器教学
自主海洋系统培训| AMC搜索
顶级自动驾驶汽车课程在线|乌德米
应用控制系统1：自动驾驶汽车：数学 + PID + MPC |乌德米
通过在线课程和课程学习自主机器人技术| edX
人工智能纳米化程序
自治系统在线课程和计划|优达学城
IoT开发人员课程的Edge AI |优达学城
自治系统MOOC和免费在线课程|慕课列表
机器人技术和自治系统研究生课程| Standford Online
移动自主系统实验室|麻省理工学院开放课件
NVIDIA cuDNN 是一个 GPU 加速的深度神经网络基元库。 Cudnn为标准例程提供了高度调整的实现，例如前向和向后卷积，汇总，归一化和激活层。 Cudnn加速了广泛使用的深度学习框架，包括Caffe2，Chainer，Keras，Matlab，Mxnet，Pytorch和Tensorflow。
NVIDIA DLSS（深度学习超级抽样）是一种时间图像，可以使用GEFORCE RTX™GPU上的专用张量核心AI处理器来提高图形性能。 DLSS利用深度学习神经网络的力量来提高框架速率并为您的游戏创造精美的，清晰的图像。
AMD FidelityFX超级分辨率（FSR）是一种开源，高质量的解决方案，用于从较低分辨率输入中产生高分辨率帧。它使用了一系列尖端深度学习算法的集合，特别着重于创建高质量的边缘，与直接在本地分辨率下进行渲染相比，具有很大的性能改进。 FSR可以实现昂贵的渲染操作的“实用性”，例如AMD RDNA™和AMD RDNA™2架构的硬件射线跟踪。
英特尔XE超级采样（XESS）是一种时间图像，可以提高AI渲染技术，可提高类似于NVIDIA的DLSS（深度学习超级抽样）的图形性能。英特尔的ARC GPU体系结构（2022年初）将拥有专用XE-Cores运行XESS的GPU。 GPU将具有用于硬件加速AI处理的XE矩阵Extenstions矩阵（XMX）发动机。 XESS将能够在没有XMX的设备上运行，包括集成图形，但是，在非智能图形卡上的XESS性能将较低，因为它将由DP4A指令提供动力。
Jupyter Notebook是一个开源Web应用程序，可让您创建和共享包含实时代码，方程式，可视化和叙事文本的文档。 Jupyter广泛用于进行数据清洁和转换，数值模拟，统计建模，数据可视化，数据科学和机器学习的行业。
Apache Spark是用于大规模数据处理的统一分析引擎。它提供了 Scala、Java、Python 和 R 中的高级 API，以及支持用于数据分析的通用计算图的优化引擎。它还支持一组丰富的高级工具，包括用于SQL和DataFrames的Spark SQL，用于机器学习的MLLIB，用于图形处理的GraphX以及用于流处理的结构化流。
SQL Server和Azure SQL的Apache Spark Connector是一个高性能连接器，它使您能够在大数据分析中使用交易数据，并在临时查询或报告中持续存在结果。该连接器允许您使用任何SQL数据库，本地或云中的任何SQL数据库作为输入数据源或Spark作业的输出数据接收器。
Apache Predictionio是开发人员，数据科学家和最终用户的开源机器学习框架。它支持事件收集，算法的部署，评估，通过REST API查询预测结果。它基于可扩展的开源服务，例如Hadoop，HBase（和其他DB），Elasticsearch，Spark，并实现所谓的Lambda架构。
Apache Kafka（CMAK）的集群管理器是管理Apache Kafka群集的工具。
BigDL是Apache Spark的分布式深度学习库。借助BIGDL，用户可以将其深度学习应用程序作为标准Spark程序编写，该应用程序可以直接在现有的Spark或Hadoop群集之上运行。
Eclipse DeepLearning4J（DL4J）是一组项目，旨在支持基于JVM（Scala，Kotlin，Clojure和Groovy）深度学习应用程序的所有需求。这意味着从原始数据开始，从任何地方和任何格式加载和预处理，以构建和调整各种简单且复杂的深度学习网络。
Deep Learning Toolbox™是一种工具，可为使用算法，预审预修的模型和应用程序设计和实施深层神经网络提供一个框架。您可以使用卷积神经网络（Convnets，CNN）和长期短期内存（LSTM）网络对图像，时间序列和文本数据进行分类和回归。您可以使用自动分化，自定义训练环和共享权重构建网络体系结构，例如生成对抗网络（GAN）和暹罗网络。借助Deep Network Designer应用程序，您可以以图形方式设计，分析和训练网络。它可以通过ONNX格式与Tensorflow™和Pytorch交换模型，并从Tensorflow-keras和Caffe中导入模型。该工具箱支持Darknet-53，Resnet-50，Nasnet，Squeezenet和许多其他预审预周审经的模型的转移学习。
增强学习Toolbox™是一种工具，可为使用增强学习算法（包括DQN，PPO，SAC和DDPG）提供应用程序，功能和Simulink®块，用于培训策略。您可以使用这些策略来为复杂的应用程序（例如资源分配，机器人技术和自治系统）实施控制器和决策算法。
深度学习HDL Toolbox™是一种工具，可为FPGA和SOCS提供原型和实施深度学习网络的功能和工具。它提供了预先建造的Bortreams，用于在支持的Xilinx®和Intel®FPGA和SOC设备上运行各种深度学习网络。分析和估算工具可让您通过探索设计，性能和资源利用权衡来自定义深度学习网络。
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.安装。原则。可扩展。 Airflow 具有模块化架构，并使用消息队列来编排任意数量的工作人员。 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 为人工智能模型（深度学习和传统机器学习）提供开源格式。它定义了可扩展的计算图模型，以及内置运算符和标准数据类型的定义。
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 是一个用于自动驾驶研究的开源模拟器。 CARLA 的开发是为了支持自动驾驶系统的开发、培训和验证。除了开源代码和协议之外，CARLA 还提供为此目的创建的开放数字资产（城市布局、建筑物、车辆），并且可以自由使用。
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 |乌德米
Top Reinforcement Learning Courses |优达学城
Reinforcement Learning Courses |斯坦福在线
Deep Learning Online Courses |英伟达
Top Deep Learning Courses Online | Coursera
Top Deep Learning Courses Online |乌德米
Learn Deep Learning with Online Courses and Lessons | edX
Deep Learning Online Course Nanodegree |优达学城
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 |乌德米
Understanding Machine Learning with Python |多元视野
How to Think About Machine Learning Algorithms |多元视野
Deep Learning Courses |斯坦福在线
Deep Learning - UW Professional & Continuing Education
Deep Learning Online Courses |哈佛大学
Machine Learning for Everyone Courses |数据营
Artificial Intelligence Expert Course: Platinum Edition |乌德米
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 |优达学城
Intro to Artificial Intelligence Course |优达学城
Edge AI for IoT Developers Course |优达学城
Reasoning: Goal Trees and Rule-Based Expert Systems |麻省理工学院开放课件
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 |乌德米
Applied Control Systems 1: autonomous cars: Math + PID + MPC |乌德米
Learn Autonomous Robotics with Online Courses and Lessons | edX
Artificial Intelligence Nanodegree program
Autonomous Systems Online Courses & Programs |优达学城
Edge AI for IoT Developers Course |优达学城
Autonomous Systems MOOC and Free Online Courses |慕课列表
Robotics and Autonomous Systems Graduate Program | Standford Online
Mobile Autonomous Systems Laboratory |麻省理工学院开放课件
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 是一个 GPU 加速的深度神经网络基元库。 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.它提供了 Scala、Java、Python 和 R 中的高级 API，以及支持用于数据分析的通用计算图的优化引擎。 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.安装。原则。可扩展。 Airflow 具有模块化架构，并使用消息队列来编排任意数量的工作人员。 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 为人工智能模型（深度学习和传统机器学习）提供开源格式。它定义了可扩展的计算图模型，以及内置运算符和标准数据类型的定义。
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 是一个用于自动驾驶研究的开源模拟器。 CARLA 的开发是为了支持自动驾驶系统的开发、培训和验证。除了开源代码和协议之外，CARLA 还提供为此目的创建的开放数字资产（城市布局、建筑物、车辆），并且可以自由使用。
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 |乌德米
Learn Computer Vision with Online Courses and Lessons | edX
Computer Vision and Image Processing Fundamentals | edX
Introduction to Computer Vision Courses |优达学城
Computer Vision Nanodegree program |优达学城
Machine Vision Course |MIT Open Courseware
Computer Vision Training Courses | NobleProg
Visual Computing Graduate Program |斯坦福在线
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 是一个 GPU 加速的深度神经网络基元库。 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 |微软Azure
Artificial Intelligence Services - Amazon Web Services (AWS)
谷歌云自然语言API
Top Natural Language Processing Courses Online |乌德米
Introduction to Natural Language Processing (NLP) |乌德米
Top Natural Language Processing Courses | Coursera
自然语言处理 | 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 |多元视野
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 是一个 GPU 加速的深度神经网络基元库。 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.它提供了 Scala、Java、Python 和 R 中的高级 API，以及支持用于数据分析的通用计算图的优化引擎。 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 具有模块化架构，并使用消息队列来编排任意数量的工作人员。 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 为人工智能模型（深度学习和传统机器学习）提供开源格式。它定义了可扩展的计算图模型，以及内置运算符和标准数据类型的定义。
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.
欧洲生物信息研究所
国家生物技术信息中心
Online Courses in Bioinformatics |ISCB - International Society for Computational Biology
生物信息学| Coursera
Top Bioinformatics Courses |乌德米
Biometrics Courses |乌德米
Learn Bioinformatics with Online Courses and Lessons | edX
Bioinformatics Graduate Certificate |哈佛延伸学校
Bioinformatics and Biostatistics | UC San Diego Extension
Bioinformatics and Proteomics - Free Online Course Materials |麻省理工学院
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使用R统计编程语言，并且是开源和开放开发的。它每年发布两个版本，并且有一个活跃的用户社区。 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.该程序将核苷酸或蛋白质序列与序列数据库进行比较并计算统计显着性。
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).借助 CUDA，开发人员能够利用 GPU 的强大功能来显着加快计算应用程序的速度。 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 快速入门指南
WSL 上的 CUDA
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 是一个 GPU 加速的深度神经网络基元库。 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 文档
MATLAB 入门
MATLAB and Simulink Training from MATLAB Academy
MathWorks 认证计划
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++ 核心指南
C++ Style Guide for ROS
学习C++
Learn C : An Interactive C Tutorial
C++ 学院
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 |哈佛
Online C Courses |哈佛大学
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 是一个重新定义和优化的代码编辑器，用于构建和调试现代 Web 和云应用程序。
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 是一款免费的 C/C++ 和 Fortran IDE，旨在满足用户最苛刻的需求。它的设计具有很强的可扩展性和完全可配置性。 Code::Blocks 围绕插件框架构建，可以使用插件进行扩展。
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++.自 2007 年以来，Boost 一直是一年一度的 Google Summer of Code 活动的参与者，学生们通过参与 Boost 库的开发来提高自己的技能。
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 提供了高效的运行时机制来确定目标平台支持哪些 OpenGL 扩展。
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.基于项目对象模型 (POM) 的概念，Maven 可以通过中央信息来管理项目的构建、报告和文档。
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 是一个高度优化的库，专注于实时应用程序。 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（另一种语言识别工具）是一个强大的解析器生成器，用于读取、处理、执行或翻译结构化文本或二进制文件。 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.它是零依赖且易于移植的。
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).它可以降低成本、缩短开发时间、推动创新并改进应用程序服务。 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.
Java 入门
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
谷歌 Java 风格指南
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.它扩展了观察者模式以支持数据/事件序列，并添加了运算符，允许您以声明方式将序列组合在一起，同时抽象出对低级线程、同步、线程安全和并发数据结构等问题的关注。
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!它被广泛用于 Google 内部的大多数 Java 项目，也被许多其他公司广泛使用。
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.它提供了 Scala、Java、Python 和 R 中的高级 API，以及支持用于数据分析的通用计算图的优化引擎。 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.它还可用于将 JSON 字符串转换为等效的 Java 对象。
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 是领先的开源自动化服务器。 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.支持任何具有 JDBC 驱动程序的数据库（这基本上意味着 - 任何数据库）。 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.它是单元测试框架的 xUnit 架构的一个实例。
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.
蟒蛇学院
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
真正的Python
The Python Open Source Computer Science Degree by Forrest Knight
Python 数据科学简介
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 可帮助您查找并安装由 Python 社区开发和共享的软件。
PyCharm 是我用过的最好的 IDE。借助 PyCharm，您可以在一个位置访问命令行、连接到数据库、创建虚拟环境并管理版本控制系统，从而避免在窗口之间不断切换，从而节省时间。
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 是一个与 Visual Studio Code 中的 Python 一起工作的扩展，可提供高性能的语言支持。在底层，Pylance 由 Microsoft 的静态类型检查工具 Pyright 提供支持。
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 是一个快速的高级网络爬行和网络抓取框架，用于爬行网站并从页面中提取结构化数据。它可用于多种用途，从数据挖掘到监控和自动化测试。
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 是一个 Python 模块，可帮助您构建复杂的批处理作业管道。 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 以各种硬拷贝格式和跨平台的交互环境生成出版质量的图形。
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.它提供了 Scala、Java、Python 和 R 中的高级 API，以及支持用于数据分析的通用计算图的优化引擎。 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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