Lists of all AI related learning materials and practical tools to get started with AI apps
- 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
- 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
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
-
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
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
Additional Reference Material
- Big Data & Machine Learning @ Google Cloud Next '17 - A collection of 49 videos
(Contributions are welcome in this space)
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
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
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
<title></title> <script src="js/navigation.js"></script>
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 |
******************
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
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 |
******************
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 |
******************
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 |
******************
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 |
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
-
Collaborative Filtering with Recurrent Neural Networks (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
-
Caffe: Convolutional architecture for fast feature embedding
-
Chainer: A powerful, flexible and intuitive framework of neural networks
-
Large-scale video classification with convolutional neural networks
-
Efficient Estimation of Word Representations in Vector Space
-
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
Stanford Syllabus CS 20SI: Tensorflow for Deep Learning Research
Comparative Study of Deep Learning Software Frameworks
** Reddit_ML- What Are You Reading**
- AI-Powered Social Bots(16 Jun 2017)
******************
Source:https://medium.com/intuitionmachine/the-deep-learning-roadmap-f0b4cac7009a
******************Source: http://www.asimovinstitute.org/neural-network-zoo/
Source: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet
Source: http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/
******************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 ******************Source: https://www.analyticsvidhya.com/blog/2015/09/full-cheatsheet-machine-learning-algorithms/
******************Source: http://datasciencefree.com/python.pdf
******************Source: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics#gs.0x1rxEA
******************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
******************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
Source: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet
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
******************Source: https://github.com/bfortuner/pytorch-cheatsheet
******************Source: http://www.wzchen.com/s/probability_cheatsheet.pdf
******************Source: https://minireference.com/static/tutorials/linear_algebra_in_4_pages.pdf
******************Source: http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf
Source: http://tutorial.math.lamar.edu/getfile.aspx?file=B,41,N