kartset/ai-learning-roadmap

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

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

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Design Thinking – An Introduction

• Stanford's virtual Crash Course in Design Thinking

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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

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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

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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

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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-education\Student-Resources\Labs\CognitiveServices\Lab_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-education\Student-Resources\Labs\Node\Lab1_SetupCheckModel.md
• Review and test in the emulator the “bot-hello” from \bot-education\Student-Resources\BOTs\Node\bot-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-education\Student-Resources\Labs\Node\Lab1_SetupCheckModel.md

Cognitive Services (Defining Intelligence) - Labs

• Complete the NLP (LUIS) Training Lab from the installed BOT Education project
  • \bot-education\Student-Resources\Labs\CognitiveServices\Lab_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-education\Student-Resources\Labs\Node\Lab1_SetupCheckModel.md
• Review and test in the emulator the “bot-hello” from
  • \bot-education\Student-Resources\BOTs\Node\bot-hello
• Review and test in the emulator the “bot-recognizers” from
  • \bot-education\Student-Resources\BOTs\Node\bot-recognizers

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Lecture Videos

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Source Berkeley

	Lecture Title	Lecturer	Semester
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 Title	Lecturer	Notes
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 Title	Lecturer	Notes
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

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Fall 2013

	Lecture Title	Lecturer	Notes
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

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Spring 2013

	Lecture Title	Lecturer	Notes
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

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Fall 2012

	Lecture Title	Lecturer	Notes
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

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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

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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)

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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/

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Microsoft Azure Algorithm Flowchart

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

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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

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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

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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

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Matplotlib

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

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

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Scikit Learn

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

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

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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

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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

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Calculus

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

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