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


Design Thinking – An Introduction


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

Lex

Polly

Rekognition

Machine Learning

Recommended Additional Resources

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


Google Cloud - Learning Material

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

Network

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

Data

AI, Big Data & Machine Learning

Additional AI Materials

(Optional) Deep Learning & Tensorflow

Additional Reference Material


IBM Watson Learning Material

(Contributions are welcome in this space)

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

Novice Training Path

Environment Set Up

Cognitive Services (Defining Intelligence)

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

Cognitive Services (Defining Intelligence)

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

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




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

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


Selected Research Papers

Stanford Syllabus CS 20SI: Tensorflow for Deep Learning Research

Comparative Study of Deep Learning Software Frameworks

** Reddit_ML- What Are You Reading**


The Many Tribes of Artificial Intelligence

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


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

The Deep Learning Roadmap

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


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

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/


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

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


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

Python


Algorithms

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


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

Python Basics

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


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

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


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

Numpy

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


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

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


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

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


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

Pandas

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


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

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


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

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


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

Tensorflow

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



Pytorch

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


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

Math

Probability

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


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

Linear Algebra

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


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

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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