gopala-kr/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

Lecture Slides

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

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/

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