Selection of resources to learn Artificial Intelligence / Machine Learning / Deep Learning
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README.md - Added FloydHub Jun 8, 2017

README.md

Links of resources for learning AI. Feel free to suggest for more resources.

Advanced Crash Courses Deep Learning by Ruslan Salakhutdinov @ KDD 2014 http://videolectures.net/kdd2014_salakhutdinov_deep_learning

Overview of DL including DBN, RBM, PGM etc which are not as popular these days. Very theoretical, dense and mathematical. Maybe not that useful for beginners. Salakhutdinov is another major player in DL. Introduction to Reinforcement Learning with Function Approximation by Rich Sutton @ NIPS 2015

http://research.microsoft.com/apps/video/?id=259577

Another intro to RL but more technical and theoretical. Rich Sutton is old school king of RL. Deep Reinforcement Learning by David Silver @ RLDM 2015

http://videolectures.net/rldm2015_silver_reinforcement_learning

Advanced intro to Deep RL as used by Deepmind on the Atari games and AlphaGo. Quite technical and requires decent understanding of RL, TD learning and Q-Learning etc. (see RL courses below). David Silver is the new school king of RL and superstar of Deepmind’s AlphaGo (which uses Deep RL). Monte Carlo Inference Methods by Ian Murray @ NIPS 2015

http://research.microsoft.com/apps/video/?id=259575

Good introduction and overview of sampling / monte carlo based methods. Not essential for a lot of DL, but good side knowledge to have. How to Grow a Mind: Statistics, Structure and Abstraction by Josh Tenenbaum @ AAAI 2012 http://videolectures.net/aaai2012_tenenbaum_grow_mind/

Completely unrelated to current DL and takes a very different approach: Bayesian Heirarchical Models. Not much success in real world yet, but I’m still a fan as the questions and problems they’re looking at feels a lot more applicable to real world than DL (e.g. one-shot learning and transfer learning, though Deepmind is looking at this with DL as well now). Two architectures for one-shot learning by Josh Tenenbaum @ NIPS 2013 http://videolectures.net/nipsworkshops2013_tenenbaum_learning

Similar to above but slightly more recent.

Optimal and Suboptimal Control in Brain and Behavior by Nathaniel Daw @ NIPS 2015 http://videolectures.net/rldm2015_daw_brain_and_behavior

Quite unrelated to DL, looks at human learning — combined with research from pyschology and neuroscience — through the computational lens of RL. Requires decent understanding of RL. Lots more one-off video lectures at: http://videolectures.net/Top/Computer_Science/Artificial_Intelligence

http://videolectures.net/Top/Computer_Science/Machine_Learning/

Massive Open Online Courses (MOOC) These are concentrated long term courses consisting of many video lectures. Ordered very roughly in the order that I recommend they are watched. Foundation / Maths

https://www.khanacademy.org/math/probability

https://www.khanacademy.org/math/linear-algebra

https://www.khanacademy.org/math/calculus-home

http://research.microsoft.com/apps/video/?id=259574

http://videolectures.net/sahd2014_lecun_deep_learning/

http://videolectures.net/rldm2015_littman_computational_reinforcement

Resource for beginners:

[1] THE NATURE OF CODE

[2] Machine Learning Theory

[3] Introduction to Computer Science and Programming in Python

[4] Seeing Theory

[5] Udacity - Intro to Artificial Intelligence

[5] Udacity - Deep Learning Foundations Course

[6] Hacker’s guide to Neural Networks

[7] CS 131 Computer Vision: Foundations and Applications

[8] Coursera Machine Learning courses

[9] Introduction to Artificial Neural Networks and Deep Learning

[10] Python Programing by Harrison

[11] Youtube channel of Harrison(from basic python to machine learning)

[12] Matlab neural network toolbox

[13] Matlab for deep learning

[14] Learning Circles

[15] Playground

[16] A.I. Experiments

[17] Machine Learning Algorithm Cheat Sheet

[18] Tombone’s Computer Vision Blog

[19] Bokeh Gallery

[20] A visual introduction to machine learning

[21] Machine Learning Mastery

[22] Everything I know about Python

[23] TensorFlow and Deep Learning without a PhD(video)

[24] Daniel Nouri Blog

[25] Programming a Perceptron in Python

[26] Improving our neural network by optimising Gradient Descent

[27] Learn TensorFlow and deep learning, without a PhD.(note)

[28] 13 Free Self-Study Books on Mathematics, Machine Learning & Deep Learning

[29] Python Program Flow Visualizer

[30] Collaborative Open Computer Science

[31] The Open Cognition Project

[32] Hvass Labs TensorFlow Tutorials

[33] Introduction to Machine Learning for Arts / Music

[34] Stanford University class CS231n: Convolutional Neural Networks for Visual Recognition by Prof. Fei-Fei Li

[35] TSne

[36] Learning Object Categories

[37] Chris Olah’s blog

[38] CS224d: Deep Learning for Natural Language Processing

[39] Jake VanderPlas Blog

[40] AIDL Blog

[41] KD Nuggets

 

 

Resource for the average user:

[1] Convolutional Neural Networks for Visual Recognition.

[1] Deep Learning, An MIT Press book

[1] Deep Learning, Datacamp

[1] Tensorflow, Pluralsight

[1] Natural Language Processing, Stanford

[2] Stanford University Deep Learning Tutorial

[3] A Guide to Deep Learning

[4] Deep Learning for Self-Driving Car

[5] Deep Learning for Self-Driving Cars (website)

[6] Deep Natural Language Processing

[7] Deep Learning documentation

[8] Deep Learning Tutorial

[9] Neural Networks and Deep Learning

[10] Deep Learning Forum

[11] Tensorflow for Deep Learning Research

[12] Pylearn2 Vision

[13] Siraj Raval youtube channel

[14] TUTORIAL ON DEEP LEARNING FOR VISION

[15] Mining of Massive Datasets

[16] Accelerate Machine Learning with the cuDNN Deep Neural Network Library

[17] Deep Learning for Computer Vision with Caffe and cuDNN

[18] Embedded Machine Learning with the cuDNN Deep Neural Network Library and Jetson TK1

[19] Deep Learning in your browser (ConvNetJS)

[20] Machine Learning with Matlab

[21] Toronto deep learning demo

[22] Fields lectures

[23] Zipfian Academy

[24] Machine Learning Recipes with Josh Gordon

[25] Microsoft Professional Program

[26] Intel for deep learning

[27] GPU Accelerated Computing with Python

[28] Import AI Newsletter

[29] Traffic Sign Recognition with TensorFlow

[30] Understand backpropagation

[31] Bigdata University

[32] Open-source language understanding for bots

[33] Pure Python Decision Trees

[34] Top 20 Python Machine Learning Open Source Projects

[35] Deep Learning, NLP, and Representations

[36] Deep Learning Research Review: Natural Language Processing

[37] Image-to-Image Translation with Conditional Adversarial Nets

[38] CMUSphinx Tutorial For Developers

[39] Machine Learning in Arts by Gene Kogan

[40] The Neural Aesthetic

[41] Visualizing High-Dimensional Space

[42] Deep-visualization-toolbox

[43] Picasso CNN visualizer

[44] Self-Driving Car

[45] NN for Self-Driving Car

[46] Simulate a Self-Driving Car

[47] CS 20SI: Tensorflow for Deep Learning Research

 

 

Resources for advanced user and researchers:

[1] Recent Researches

[2] The Morning Paper

[3] Most Cited Deep Learning Papers

[4] Arxiv Sanity Preserver

[5] Uncertainty in Deep Learning

[6] Deep Patient

[7] A space-time delay neural network

[8] Google Cloud Natural Language API

[8] FloydHub - Heroku for DL

[9] Blue Brain Project

[10] Whole genome sequencing resource

[11] Sorta Insightful

[12] The Eyescream Project

[13] Generative Adversarial Networks

 

Open source libraries/repositories/Framework:

[1] Tensor Flow

[2] Keras

[3] Scikit-learn

[4] Universe

[4] Lua

[5] Torch

[6] Theano

[7] Machine Learning Library (MLlib)

[8] UC Irvine Machine Learning Repository

[9] The CIFAR-10 dataset

[10] NeuPy

[11] Deeplearning4j

[12] ImageNet

[13] Seaborn

[14] MLdata

[15] CNTK

[16] Natural Language Toolkit(NLTK)

[17] Spacy

[18] CoreNLP

[19] Requests: HTTP for Humans

[20] Computational Healthcare library

[21] Blaze

[22] Dask

[23] Array Express

[24] Pillow

[25] HTM

[26] Pybrain

[27] Nilearn

[28] Pattern

[29] Fuel

[30] Pylearn2

[31] Bob

[32] Skdata

[33] MILK

[34] IEPY

[35] Quepy

[36] nupic

[37] Hebel

[38] Ramp

[39] Machine-learning-samples

[40] H2O

[41] Optunity

[42] Awesome Public Datasets

[43] PyTorch

[44] Kubernetes

[45] Generative Adversarial Text-to-Image Synthesis

[46] Pydata

[47] Open Data Kit (ODK)

[48] Open Detection

[49] Mycroft

[50] Medical Image Net

[51] Biorxiv (archive and distribution service for unpublished preprints in the life sciences)

[52] Udacity Self-Driving Car Simulator

[53] List of Medical Datasets and repositories

 

All video materials:

[1] Machine Learning Recipes with Josh Gordon

[2] Deep Learning for Vision with Caffe framework

[3] Stanford University Machine Learning course (By Prof. Andrew Ng)

[4] Deep Learning for Computer Vision by Dr. Rob Fergus

[5] Caltech Machine Learning Course

[6] Machine Learning and AI via Brain simulations

[7] Deep Learning of Representations (Google Talk)

[8] Data School

[9] How to run Neural Nets on GPUs’ by Melanie Warrick

[10] TensorFlow and Deep Learning without a PhD

[11] Youtube channel of Harrison(from basic python to machine learning)

[12] Siraj Raval youtube channel

[13] Machine Learning Prepare Data Tutorial

[14] Hvass Laboratories

Brain-Computer Interfacing:

[1] All BCI resources at one place

 

AI companies / organisations:

[1] DeepMind

[2] MILA

[3] IBM Watson

[4] The Swiss AI Lab (IDSIA)

[5] Comma AI

[6] Indico

[7] Osaro

[8] Cloudera

[9] Geometric Intelligence

[10] Skymind

[11] MetaMind

[12] Iris AI

[13] Feedzai

[14] Loomai

[15] BenevolentAI

[16] Baidu Research

[17] Rasa AI

[18] AI Gym

[19] Nervana

[20] CrowdAI

[21] Idiap Research Institute

[22] Maluuba

[23] Neurala

[24] Artificial Intelligence Group at UCSD

[25] Turi

[26] Enlitic

[27] Element AI

[28] Accel AI

[29] Datalog AI

[30] Fast AI

[31] Applied Brain Research

[32] Neuraldesigner

[33] Autox

[34] Niramai

[35] Isenses

[36] MedGenome

[37] Recursion Pharmaceuticals, Inc.

[38] Geometric Intelligence

[39] jukedeck (AI Musician)

[40] Galaxy AI

[41] Atomwise

[42] DeepArt

 

 

AI Personalities:

Yangqing Jia

Evan Shelhamer

Demis Hassabis

Josh Tenenbaum

Yoshua Bengio

Brendan J. Frey

Arun Kumar

Mostafa Samir

Andrej Karpathy

Justin Johnson

Fei-Fei Li

Juan Carlos Niebles

Carl Edward Rasmussen

Geoffrey E. Hinton

Yann LeCun

Neil Jacobstein

Andrew Ng

Jeffrey Dean

Rajat Monga

Manohar Paluri

Joaquin Quinonero Candela

Bhaskar Mitra

Pushmeet Kohli

Ilya Sutskever

Greg Brockman

Reference: http://aimedicines.com/2017/03/17/all-ai-resources-at-one-place/