gopala-kr•Nov 30, 2024
ai-learning-roadmap
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-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
Lecture Videos
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
Gibbs' Sampling
Michael Liang
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
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
RedditML- 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
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/mlcheatsheet/blob/master/supervised_learning.ipynb
Tensorflow
Source: https://github.com/aymericdamien/TensorFlow-Examples/blob/master/notebooks/1Introduction/basicoperations.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/linearalgebrain4pages.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
