Machine Learning
Getting Started
DeepLearning.TV YouTube playlist -- good starter!
Tuts
UFLDL Stanford (Deep Learning) Tutorial
Principles of training multi-layer neural network using backpropagation <-- Great visual guide!
Courses
Neural Networks for Machine Learning — Geoffrey Hinton, UToronto
- Coursera course - Vids (on YouTube) - same, better organized - Intro vid for course - Hinton's homepage - Bayesian Nets Tutorial -- helpful for later parts of Hinton
Deep learning at Oxford 2015 (Nando de Freitas)
Notes for Andrew Ng's Coursera course.
Hugo Larochelle: Neural networks class - Université de Sherbrooke
Boltzmann Machines
Think: Hopfield Net, but each neuron stochastically has state 0 or 1. Prob(state <- 1) = sigma(-z) where z = preactivation.
Boltzmann distribution: Wikipedia Susskind lecture
Hopfield to Boltzmann http://haohanw.blogspot.co.uk/2015/01/boltzmann-machine.html
Hinton's Lecture, then:
https://en.wikipedia.org/wiki/Boltzmann_machine
http://www.scholarpedia.org/article/Boltzmann_machine
Hinton (2010) -- A Practical Guide to Training Restricted Boltzmann Machines
https://www.researchgate.net/publication/242509302_Learning_and_relearning_in_Boltzmann_machines
Papers
Applying Deep Learning To Enhance Momentum Trading Strategies In Stocks
- Hinton, Salakhutdinov (2006) -- Reducing the Dimensionality of Data with Neural Networks
Books
Nielsen -- Neural Networks and Deep Learning <-- online book
http://www.deeplearningbook.org/
https://page.mi.fu-berlin.de/rojas/neural/ <-- Online book
S/W
http://playground.tensorflow.org
TensorFlow in IPython YouTube (5 vids)
SwiftNet <-- My own back propagating NN (in Swift)
Misc
Links: https://github.com/memo/ai-resources
http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ <-- Great article!
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://www.youtube.com/watch?v=gfPUWwBkXZY <-- Hopfield vid
http://www.gitxiv.com/ <-- Amazing projects here!
