Collection of notes on AI/ML

Watch this video first

Tensorflow and Deep Learning

Deep Visualization Toolbox

Youtube Channels

Sentdex

ML @ Berkeley

Courses on AI/ML

Prerequisites

Linear Algebra @ Udacity

Linear Algebra @ Khan Academy

Probability @ UBC

Probability and Statistics @ Stanford

Statistics @ Stanford

Statistics

Data Science @ Kaggle

Data Science @ Berkeley

AI/ML

Machine Learning @ Google

Machine Learning @ Coursera

Machine Learning @ Udacity

Machine Learning @ Amazon

Neural Networks for Machine Learning @ Coursera

Deep Learning by Google @ Udacity

Deep Learning for NLP @ Stanford

Creative Applications of DL with Tensorflow

Neural Networks for ML

Practical Deep Learning for Coders -1

Introduction to Deep Learning

Deep Learning for NLP

NLP with Deep Learning @ Stanford

Self Training/Tutorials

Python

7 Steps to Machine Learning with Python

Kaggle Python Tutorial

Intro

Machine Learning is fun - Part 1

Machine Learning is fun - Part 2

Machine Learning is fun - Part 3

Machine Learning is fun - Part 4

Machine Learning is fun - Part 5

Machine Learning is fun - Part 6

Machine Learning is fun - Part 7

Machine Learning is fun - Part 8

Machine Learning for Humans - Part 1.0

Machine Learning for Humans - Part 2.1

Machine Learning for Humans - Part 2.2

Machine Learning for Humans - Part 2.3

Machine Learning for Humans - Part 3.0

Machine Learning for Humans - Part 4.0

Machine Learning for Humans - Part 5.0

Machine Learning for Humans - Appendix

Scipy and Numpy

scikit learn

Pytorch

Tutorial References

Machine Learning

Activation and Loss Functions

Bias

Perceptron

Regression

Gradient Descent

Generative Learning

Support Vector Machine

Backpropagation

Deep Learning

Optimization and Dimensionality Reduction

Long Short Term Memory

Convolutional Neural Networks CNN

Recurrent Neural Networks RNN

Reinforcement Learning

Generative Adversarial Network GAN

NLP

Deep Learning and NLP

Word Vectors

Encoder Decoders

Gensim

Gensim Tutorial

Math

Linear Algebra

Probability

Understanding Bayes Theorem

Review of Probability Theory

Probably Theory review for ML

Calculus

Calculus Overview

How to Understand Derivatives A

How to Understand Derivatives B

Vector Calculus: Gradient

Differential Calculus

Activation Functions

Tensorflow

Keras

Keras Simple Tutorial

Tools

Anaconda

Jupyter

Notebooks

Common

Data Repositories

UCI

Redit Data Sets

All of them

Public Image Data Sets

IRIS Flowers Data Set

Breast Cancer Data Set

Learning Platforms

OpenAI Gym

Kaggle

Algebraic Visualizations

Tensorflow

Tensorflow;

Tensorflow Sandbox

Tensorflow Slides

Apache

Apache PredictionIO

Algorithms

Algorithmia

Libraries

Panda

Panda Intro

Seaborn

Organizations

OpenAI

DeepMind

Google Research

Google.AI

AWS AI

Facebook AI Research

Microsoft Research

Bauidu Research

Getting Started

Installing Anaconda

MacOS Installation notes

Install Libraries not available from Conda Repo

%source activate <Conda-Env>
%pip install <package>

Update libraries installed by pip

%pip list --outdated
%pip install <package> --upgrade

Install Libraries available from Conda Repo

%source activate <Conda-Env>
%conda list # to show currently installed packages
%conda install --channel <url> <package>

# search
%conda search <package>

# Tensorflow
%conda install -c conda-forge tensorflow
# Keras
%conda install -c conda-forge keras

# update
%conda update <package>

References

Deep Learning Glossary

AI Blog

Cheat Sheet

AI Tools For Personal Use

AI Tools For Business Use

Comparing Learning Algorithms