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:
[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
[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
[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
[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] Natural Language Processing, Stanford
[2] Stanford University Deep Learning Tutorial
[4] Deep Learning for Self-Driving Car
[5] Deep Learning for Self-Driving Cars (website)
[6] Deep Natural Language Processing
[7] Deep Learning documentation
[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
[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
[44] Self-Driving Car
[46] Simulate a Self-Driving Car
[47] CS 20SI: Tensorflow for Deep Learning Research
Resources for advanced user and researchers:
[3] Most Cited Deep Learning Papers
[5] Uncertainty in Deep Learning
[6] Deep Patient
[7] A space-time delay neural network
[8] Google Cloud Natural Language API
[10] Whole genome sequencing resource
[11] Sorta Insightful
[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
[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
[40] H2O
[41] Optunity
[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
[5] Comma AI
[6] Indico
[7] Osaro
[8] Cloudera
[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
[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
[32] Neuraldesigner
[33] Autox
[34] Niramai
[35] Isenses
[36] MedGenome
[37] Recursion Pharmaceuticals, Inc.
[40] Galaxy AI
[41] Atomwise
[42] DeepArt
AI Personalities:
Reference: http://aimedicines.com/2017/03/17/all-ai-resources-at-one-place/