Difference between revisions of "Machine Learning"

Revision as of 09:59, 25 August 2016

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

Karpathy -- CS231n

Boltzmann Machines

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

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

Oxford AI/Trader

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!