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Lists of all AI related learning materials and practical tools to get started with AI apps

Design Thinking – An Introduction

  • Stanford's virtual Crash Course in Design Thinking

Amazon Web Services Learning Material

  • AWS AI Session– The session provides an overview of all Amazon AI technology offerings (Lex, Polly, Rekognition, ML, and Deep Learning AMI)

Self-Paced Labs

AWS self-paced labs provide hands-on practice in a live AWS environment with AWS services and real-world cloud scenarios. Follow step-by-step instructions to learn a service, practice a use case, or prepare for AWS Certification.

Introductory Lab

  • Introduction to AWS Lambda

Lex

  • Introduction to Amazon Lex
  • Amazon Lex Webinar
  • Amazon Lex: AWS conversational interface (chat bot)
  • Documentation

Polly

  • Introduction to Amazon Polly
  • Amazon Polly Webinar -
  • Amazon Polly – AWS Text To Speech (TTS) service
  • Documentation
    • What is Amazon Polly?
    • Developer Resources

Rekognition

  • Introduction to Amazon Rekognition
  • Amazon Rekognition - Deep Learning-Based Image Analysis Webinar
  • Amazon Rekognition – AWS image recognition service
  • Documentation – What is Amazon Rekognition?

Machine Learning

  • Machine Learning

  • Session 1 – Empowering Developers to Build Smart Applications

  • Session 2 - Predicting Customer Churn with Amazon Machine Learning

  • AWS Machine Learning – End to end, managed service for creating and testing ML models and then deploying those models into production

  • Documentation

    • What is Amazon Machine Learning?
    • Developer Resources

  • AWS Deep Learning AMI – Amazon Machine Image (AMI) optimized for deep learning efforts

Recommended Additional Resources

Take your skills to the next level with fundamental, advanced, and expert level labs.

  • Creating Amazon EC2 Instances with Microsoft Windows
  • Building Your First Amazon Virtual Private Cloud (VPC)
  • Working with AWS CodeCommit on Windows
  • Working with Amazon DynamoDB

Google Cloud - Learning Material

Below is the learning material that will help you learn about Google Cloud.

Network

  • Networking 101 – 43 mins

The codelab provides common cloud developer experience as follows:

  • Set up your lab environment and learn how to work with your GCP environment.
  • Use of common open source tools to explore your network around the world.
  • Deploy a common use case: use of HTTP Load Balancing and Managed Instance Groups to host a scalable, multi-region web server.
  • Testing and monitoring your network and instances.
  • Cleanup.

Developing Solutions for Google Cloud Platform – 8 hours

Infrastructure

  • Build a Slack Bot with Node.js on Kubernotes – 43 mins
  • Creating a Virtual Machine – 10 mins
  • Getting Started with App Engine (Python) – 13 mins

Data

  • Introduction to Google Cloud Data Prep – 7 mins
  • Create a Managed MySQL database with Cloud SQL – 19 mins
  • Upload Objects to Cloud Storage – 11 mins

AI, Big Data & Machine Learning

  • Introduction to Google Cloud Machine Learning – 1 hour
  • Machine Learning APIs by Example – 30 min
  • Google Cloud Platform Big Data and Machine Learning Fundamentals

Additional AI Materials

  • Auto-awesome: Advanced Data Science on Google Cloud Platform – 45 min
  • Run a Big Data Text Processing Pipeline in Cloud Dataflow – 21 min
  • Image Classification Using Cloud ML Engine & Datalab – 58 min
  • Structured Data Regression Using Cloud ML Engine & Datalab – 58 min

(Optional) Deep Learning & Tensorflow

  • Tensorflow and Deep Learning Tutorial – 2:35 hours
  • Deep Learning Course – advanced users only

Additional Reference Material

  • Big Data & Machine Learning @ Google Cloud Next '17 - A collection of 49 videos

IBM Watson Learning Material

(Contributions are welcome in this space)

  • IBM Watson Overview
  • IBM Watson Cognitive APIs
  • IBM Watson Knowledge Studio

Visual Studio

UCI datasets

Microsoft Chat Bots Learning Material

Skills Prerequisite

  • Git and Github
  • NodeJS
  • VS Code IDE

Training Paths

If you have the above Prerequisite skills, then take Advanced Training Path else take Novice Training Path.

Prerequisite Tutorials

  • Git and Github
  • Node.js
    • Node.js Tutorials for Beginners
    • Node.js Tutorial in VS Code
  • Introduction To Visual Studio Code

Novice Training Path

Environment Set Up

  • Download and Install Git
  • Set up GitHub Account_
  • Download and Install NodeJS
  • Download and Install IDE - Visual Studio Code
  • Download and Install the Bot Framework Emulator
  • Git clone the Bot Education project - git clone
  • Set Up Azure Free Trial Account

Cognitive Services (Defining Intelligence)

  • Read Cognitive Services ADS Education Deck – git clone
  • Review the guide for Understanding Natural language with LUIS
  • Complete the NLP (LUIS) Training Lab from the installed Bot Education project – bot-educationStudent-ResourcesLabsCognitiveServicesLab_SetupLanguageModel.md

Bot Framework (Building Chat Bots)

  • Read Bot Framework ADS Education Deck from downloaded - (Your Path)bot-extras
  • Review Bot Framework documentation (Core Concepts, Bot Builder for NodeJS, and Bot Intelligence) -
  • Setup local environment and run emulator from the installed Bot Education project – bot-educationStudent-ResourcesLabsNodeLab1_SetupCheckModel.md
  • Review and test in the emulator the “bot-hello” from bot-educationStudent-ResourcesBOTsNodebot-hello

Advanced Training Path

Environment Set Up

  • Download and Install Git
  • Set up GitHub Account_
  • Download and Install NodeJS
  • Download and Install IDE - Visual Studio Code
  • Download and Install the Bot Framework Emulator
  • Git clone the Bot Education project - git clone
  • Set Up Azure Free Trial Account
  • Git clone the Bot Builder Samples – git clone

Cognitive Services (Defining Intelligence)

  • Read Cognitive Services ADS Education Deck – git clone
  • Review the guide for Understanding Natural language with LUIS

Bot Framework (Building Chat Bots)

  • Read Bot Framework ADS Education Deck from downloaded - (Your Path)bot-extras
  • Review Bot Framework documentation (Core Concepts, Bot Builder for NodeJS, and Bot Intelligence) -
  • Setup local environment and run emulator from the installed Bot Education project – bot-educationStudent-ResourcesLabsNodeLab1_SetupCheckModel.md

Cognitive Services (Defining Intelligence) - Labs

  • Complete the NLP (LUIS) Training Lab from the installed BOT Education project
    • bot-educationStudent-ResourcesLabsCognitiveServicesLab_SetupLanguageModel.md
  • Review, Deploy and run the LUIS BOT sample

Bot Framework (Building Chat Bots) – Labs

  • Setup local environment and run emulator from the installed Bot Education project
    • bot-educationStudent-ResourcesLabsNodeLab1_SetupCheckModel.md
  • Review and test in the emulator the “bot-hello” from
    • bot-educationStudent-ResourcesBOTsNodebot-hello
  • Review and test in the emulator the “bot-recognizers” from
    • bot-educationStudent-ResourcesBOTsNodebot-recognizers

Lecture Videos

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Source Berkeley

Lecture TitleLecturerSemester
Lecture 1 Introduction Dan Klein Fall 2012
Lecture 2 Uninformed Search Dan Klein Fall 2012
Lecture 3 Informed Search Dan Klein Fall 2012
Lecture 4 Constraint Satisfaction Problems I Dan Klein Fall 2012
Lecture 5 Constraint Satisfaction Problems II Dan Klein Fall 2012
Lecture 6 Adversarial Search Dan Klein Fall 2012
Lecture 7 Expectimax and Utilities Dan Klein Fall 2012
Lecture 8 Markov Decision Processes I Dan Klein Fall 2012
Lecture 9 Markov Decision Processes II Dan Klein Fall 2012
Lecture 10 Reinforcement Learning I Dan Klein Fall 2012
Lecture 11 Reinforcement Learning II Dan Klein Fall 2012
Lecture 12 Probability Pieter Abbeel Spring 2014
Lecture 13 Markov Models Pieter Abbeel Spring 2014
Lecture 14 Hidden Markov Models Dan Klein Fall 2013
Lecture 15 Applications of HMMs / Speech Pieter Abbeel Spring 2014
Lecture 16 Bayes' Nets: Representation Pieter Abbeel Spring 2014
Lecture 17 Bayes' Nets: Independence Pieter Abbeel Spring 2014
Lecture 18 Bayes' Nets: Inference Pieter Abbeel Spring 2014
Lecture 19 Bayes' Nets: Sampling Pieter Abbeel Fall 2013
Lecture 20 Decision Diagrams / Value of Perfect Information Pieter Abbeel Spring 2014
Lecture 21 Machine Learning: Naive Bayes Nicholas Hay Spring 2014
Lecture 22 Machine Learning: Perceptrons Pieter Abbeel Spring 2014
Lecture 23 Machine Learning: Kernels and Clustering Pieter Abbeel Spring 2014
Lecture 24 Advanced Applications: NLP, Games, and Robotic Cars Pieter Abbeel Spring 2014
Lecture 25 Advanced Applications: Computer Vision and Robotics Pieter Abbeel Spring 2014

Additionally, there are additional Step-By-Step videos which supplement the lecture's materials. These videos are listed below:

Lecture TitleLecturerNotes
SBS-1 DFS and BFS Pieter Abbeel Lec: Uninformed Search
SBS-2 A* Search Pieter Abbeel Lec: Informed Search
SBS-3 Alpha-Beta Pruning Pieter Abbeel Lec: Adversarial Search
SBS-4 D-Separation Pieter Abbeel Lec: Bayes' Nets: Independence
SBS-5 Elimination of One Variable Pieter Abbeel Lec: Bayes' Nets: Inference
SBS-6 Variable Elimination Pieter Abbeel Lec: Bayes' Nets: Inference
SBS-7 Sampling Pieter Abbeel Lec: Bayes' Nets: Sampling
SBS-8 Maximum Likelihood Pieter Abbeel Lec: Machine Learning: Naive Bayes
SBS-9 Laplace Smoothing Pieter Abbeel Lec: Machine Learning: Naive Bayes
SBS-10 Perceptrons Pieter Abbeel Lec: Machine Learning: Perceptrons



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Per-Semester Video Archive(Berkeley)

The lecture videos from the most recent offerings are posted below.

Spring 2014 Lecture Videos
Fall 2013 Lecture Videos
Spring 2013 Lecture Videos
Fall 2012 Lecture Videos

Spring 2014

Lecture TitleLecturerNotes
Lecture 1 Introduction Pieter Abbeel
Lecture 2 Uninformed Search Pieter Abbeel
Lecture 3 Informed Search Pieter Abbeel
Lecture 4 Constraint Satisfaction Problems I Pieter Abbeel Recording is a bit flaky, see Fall 2013 Lecture 4 for alternative
Lecture 5 Constraint Satisfaction Problems II Pieter Abbeel
Lecture 6 Adversarial Search Pieter Abbeel
Lecture 7 Expectimax and Utilities Pieter Abbeel
Lecture 8 Markov Decision Processes I Pieter Abbeel
Lecture 9 Markov Decision Processes II Pieter Abbeel
Lecture 10 Reinforcement Learning I Pieter Abbeel
Lecture 11 Reinforcement Learning II Pieter Abbeel
Lecture 12 Probability Pieter Abbeel
Lecture 13 Markov Models Pieter Abbeel
Lecture 14 Hidden Markov Models Pieter Abbeel Recording is a bit flaky, see Fall 2013 Lecture 18 for alternative
Lecture 15 Applications of HMMs / Speech Pieter Abbeel
Lecture 16 Bayes' Nets: Representation Pieter Abbeel
Lecture 17 Bayes' Nets: Independence Pieter Abbeel
Lecture 18 Bayes' Nets: Inference Pieter Abbeel
Lecture 19 Bayes' Nets: Sampling Pieter Abbeel Unrecorded, see Fall 2013 Lecture 16
Lecture 20 Decision Diagrams / Value of Perfect Information Pieter Abbeel
Lecture 21 Machine Learning: Naive Bayes Nicholas Hay
Lecture 22 Machine Learning: Perceptrons Pieter Abbeel
Lecture 23 Machine Learning: Kernels and Clustering Pieter Abbeel
Lecture 24 Advanced Applications: NLP, Games, and Robotic Cars Pieter Abbeel
Lecture 25 Advanced Applications: Computer Vision and Robotics Pieter Abbeel
Lecture 26 Conclusion Pieter Abbeel Unrecorded


******************

Fall 2013

Lecture TitleLecturerNotes
Lecture 1 Introduction Dan Klein
Lecture 2 Uninformed Search Dan Klein
Lecture 3 Informed Search Dan Klein
Lecture 4 Constraint Satisfaction Problems I Dan Klein
Lecture 5 Constraint Satisfaction Problems II Dan Klein
Lecture 6 Adversarial Search Dan Klein
Lecture 7 Expectimax and Utilities Dan Klein
Lecture 8 Markov Decision Processes I Dan Klein
Lecture 9 Markov Decision Processes II Dan Klein
Lecture 10 Reinforcement Learning I Dan Klein
Lecture 11 Reinforcement Learning II Dan Klein
Lecture 12 Probability Pieter Abbeel
Lecture 13 Bayes' Nets: Representation Pieter Abbeel
Lecture 14 Bayes' Nets: Independence Dan Klein
Lecture 15 Bayes' Nets: Inference Pieter Abbeel
Lecture 16 Bayes' Nets: Sampling Pieter Abbeel
Lecture 17 Decision Diagrams / Value of Perfect Information Pieter Abbeel
Lecture 18 Hidden Markov Models Dan Klein
Lecture 19 Applications of HMMs / Speech Dan Klein
Lecture 20 Machine Learning: Naive Bayes Dan Klein
Lecture 21 Machine Learning: Perceptrons Dan Klein
Lecture 22 Machine Learning: Kernels and Clustering Pieter Abbeel
Lecture 23 Machine Learning: Decision Trees and Neural Nets Pieter Abbeel
Lecture 24 Advanced Applications: NLP and Robotic Cars Dan Klein Unrecorded, see Spring 2013 Lecture 24
Lecture 25 Advanced Applications: Computer Vision and Robotics Pieter Abbeel
Lecture 26 Conclusion Dan Klein,
Pieter Abbeel		 Unrecorded

******************

Spring 2013

Lecture TitleLecturerNotes
Lecture 1 Introduction Pieter Abbeel Video Down
Lecture 2 Uninformed Search Pieter Abbeel
Lecture 3 Informed Search Pieter Abbeel
Lecture 4 Constraint Satisfaction Problems I Pieter Abbeel
Lecture 5 Constraint Satisfaction Problems II Pieter Abbeel Unrecorded, see Fall 2012 Lecture 5
Lecture 6 Adversarial Search Pieter Abbeel
Lecture 7 Expectimax and Utilities Pieter Abbeel
Lecture 8 Markov Decision Processes I Pieter Abbeel
Lecture 9 Markov Decision Processes II Pieter Abbeel
Lecture 10 Reinforcement Learning I Pieter Abbeel
Lecture 11 Reinforcement Learning II Pieter Abbeel
Lecture 12 Probability Pieter Abbeel
Lecture 13 Bayes' Nets: Representation Pieter Abbeel
Lecture 14 Bayes' Nets: Independence Pieter Abbeel
Lecture 15 Bayes' Nets: Inference Pieter Abbeel
Lecture 16 Bayes' Nets: Sampling Pieter Abbeel
Lecture 17 Decision Diagrams / Value of Perfect Information Pieter Abbeel
Lecture 18 Hidden Markov Models Pieter Abbeel
Lecture 19 Applications of HMMs / Speech Pieter Abbeel
Lecture 20 Machine Learning: Naive Bayes Pieter Abbeel
Lecture 21 Machine Learning: Perceptrons I Nicholas Hay
Lecture 22 Machine Learning: Perceptrons II Pieter Abbeel
Lecture 23 Machine Learning: Kernels and Clustering Pieter Abbeel
Lecture 24 Advanced Applications: NLP and Robotic Cars Pieter Abbeel
Lecture 25 Advanced Applications: Computer Vision and Robotics Pieter Abbeel
Lecture 26 Conclusion Pieter Abbeel Unrecorded

******************

Fall 2012

Lecture TitleLecturerNotes
Lecture 1 Introduction Dan Klein
Lecture 2 Uninformed Search Dan Klein
Lecture 3 Informed Search Dan Klein
Lecture 4 Constraint Satisfaction Problems I Dan Klein
Lecture 5 Constraint Satisfaction Problems II Dan Klein
Lecture 6 Adversarial Search Dan Klein
Lecture 7 Expectimax and Utilities Dan Klein
Lecture 8 Markov Decision Processes I Dan Klein
Lecture 9 Markov Decision Processes II Dan Klein
Lecture 10 Reinforcement Learning I Dan Klein
Lecture 11 Reinforcement Learning II Dan Klein
Lecture 12 Probability Pieter Abbeel
Lecture 13 Bayes' Nets: Representation Pieter Abbeel
Lecture 14 Bayes' Nets: Independence Pieter Abbeel
Lecture 15 Bayes' Nets: Inference Pieter Abbeel
Lecture 16 Bayes' Nets: Sampling Pieter Abbeel
Lecture 17 Decision Diagrams / Value of Perfect Information Pieter Abbeel
Lecture 18 Hidden Markov Models Pieter Abbeel
Lecture 19 Applications of HMMs / Speech Dan Klein
Lecture 20 Machine Learning: Naive Bayes Dan Klein
Lecture 21 Machine Learning: Perceptrons Dan Klein
Lecture 22 Machine Learning: Kernels and Clustering Dan Klein
Lecture 23 Machine Learning: Decision Trees and Neural Nets Pieter Abbeel
Lecture 24 Advanced Applications: Computer Vision and Robotics Pieter Abbeel
Lecture 25 Advanced Applications: NLP and Robotic Cars Dan Klein,
Pieter Abbeel		 Unrecorded
Lecture 26 Conclusion Dan Klein,
Pieter Abbeel		 Unrecorded



******************

Lecture Slides

Here is the complete set of lecture slides, including videos, and videos of demos run in lecture: Slides [~3 GB].

The list below contains all the lecture powerpoint slides:

  • Lecture 1: Introduction
  • Lecture 2: Uninformed Search
  • Lecture 3: Informed Search
  • Lecture 4: CSPs I
  • Lecture 5: CSPs II
  • Lecture 6: Adversarial Search
  • Lecture 7: Expectimax Search and Utilities
  • Lecture 8: MDPs I
  • Lecture 9: MDPs II
  • Lecture 10: Reinforcement Learning I
  • Lecture 11: Reinforcement Learning II
  • Lecture 12: Probability
  • Lecture 13: Markov Models
  • Lecture 14: Hidden Markov Models
  • Lecture 15: Particle Filters and Applications of HMMs
  • Lecture 16: Bayes Nets I: Representation
  • Lecture 17: Bayes Nets II: Independence
  • Lecture 18: Bayes Nets III: Inference
  • Lecture 19: Bayes Nets IV: Sampling
  • Lecture 20: Decision Diagrams and VPI
  • Lecture 21: Naive Bayes
  • Lecture 22: Perceptron
  • Lecture 23: Kernels and Clustering
  • Lecture 24: Advanced Applications (NLP, Games, Cars)
  • Lecture 25: Advanced Applications (Computer Vision and Robotics)
  • Lecture 26: Conclusion

The source files for all live in-lecture demos are being prepared from Berkeley AI for release

Selected Research Papers

  • Latest arxiv paper submissionson AI

  • Peter Norvig-Teach Yourself Programming in Ten Years

  • How to do Research At the MIT AI Lab

  • A Roadmap towards Machine Intelligence

  • Collaborative Filtering with Recurrent Neural Networks (2016)

  • Wide & Deep Learning for Recommender Systems (2016)

  • Deep Collaborative Filtering via Marginalized Denoising Auto-encoder (2015)

  • Nonparametric bayesian multitask collaborative filtering (2013)

  • Tensorflow: Large-scale machine learning on heterogeneous distributed systems

  • https://infoscience.epfl.ch/record/82802/files/rr02-46.pdf

  • Theano: A CPU and GPU math expression compiler.

  • Caffe: Convolutional architecture for fast feature embedding

  • Chainer: A powerful, flexible and intuitive framework of neural networks

  • Large Scale Distributed Deep Networks

  • Large-scale video classification with convolutional neural networks

  • Efficient Estimation of Word Representations in Vector Space

  • Grammar as a Foreign Language

  • Going Deeper with Convolutions

  • ON RECTIFIED LINEAR UNITS FOR SPEECH PROCESSING

  • Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups.

  • Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

  • google turning its lucrative web search over to AI machines

Stanford Syllabus CS 20SI: Tensorflow for Deep Learning Research

  • Crowd-Based Personalized Natural Language Explanations for Recommendations

Comparative Study of Deep Learning Software Frameworks

** Reddit_ML- What Are You Reading**

  • AI-Powered Social Bots(16 Jun 2017)

The Many Tribes of Artificial Intelligence

Source:https://medium.com/intuitionmachine/infographic-best-practices-in-training-deep-learning-networks-b8a3df1db53


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The Deep Learning Roadmap

Source:https://medium.com/intuitionmachine/the-deep-learning-roadmap-f0b4cac7009a


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Best Practices for Training Deep Learning Networks

Source: https://medium.com/intuitionmachine/infographic-best-practices-in-training-deep-learning-networks-b8a3df1db53


ML/DL Cheatsheets

Neural Network Architectures

Source: http://www.asimovinstitute.org/neural-network-zoo/


Microsoft Azure Algorithm Flowchart

Source: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet


SAS Algorithm Flowchart

Source: http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/


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Algorithm Summary

Source: http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/


****************** Source: http://thinkbigdata.in/best-known-machine-learning-algorithms-infographic/


****************** ### Algorithm Pro/Con Source: https://blog.dataiku.com/machine-learning-explained-algorithms-are-your-friend


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Python

Algorithms

Source: https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/


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Python Basics

Source: http://datasciencefree.com/python.pdf


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Source: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA


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Numpy

Source: https://www.dataquest.io/blog/numpy-cheat-sheet/


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Source: http://datasciencefree.com/numpy.pdf


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Source: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.Nw3V6CE


Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/numpy/numpy.ipynb


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Pandas

Source: http://datasciencefree.com/pandas.pdf


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Source: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.S4P4T=U


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Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/pandas/pandas.ipynb


Matplotlib

Source: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet


Source: https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/matplotlib/matplotlib.ipynb


Scikit Learn

Source: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.fZ2A1Jk


Source: http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html


Source: https://github.com/rcompton/ml_cheat_sheet/blob/master/supervised_learning.ipynb


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Tensorflow

Source: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1_Introduction/basic_operations.ipynb


Pytorch

Source: https://github.com/bfortuner/pytorch-cheatsheet


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Math

Probability

Source: http://www.wzchen.com/s/probability_cheatsheet.pdf


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Linear Algebra

Source: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf


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Statistics

Source: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf


Calculus

Source: http://tutorial.math.lamar.edu/getfile.aspx?file=B,41,N


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