Difference between revisions of "Machine Learning"

Revision as of 10:03, 24 August 2016

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.

Papers

Applying Deep Learning To Enhance Momentum Trading Strategies In Stocks

- Hinton (2010) -- A Practical Guide to Training Restricted Boltzmann Machines
- Hinton, Salakhutdinov (2006) -- Reducing the Dimensionality of Data with Neural Networks

Books

Nielsen -- Neural Networks and Deep Learning <-- online book

http://www.deeplearningbook.org/

S/W

http://playground.tensorflow.org

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/

Hopfield to Boltzmann http://haohanw.blogspot.co.uk/2015/01/boltzmann-machine.html

https://www.youtube.com/watch?v=gfPUWwBkXZY <-- Hopfield vid