View source for Machine Learning

== Tuts ==
[http://ufldl.stanford.edu/tutorial/ UFLDL Stanford (Deep Learning) Tutorial]

[http://home.agh.edu.pl/~vlsi/AI/backp_t_en/backprop.html Principles of training multi-layer neural network using backpropagation] <-- Great visual guide!  


== Courses ==
Neural Networks for Machine Learning — Geoffrey Hinton, UToronto
 - [https://www.coursera.org/learn/neural-networks Coursera course]
 - [https://www.youtube.com/playlist?list=PLoRl3Ht4JOcdU872GhiYWf6jwrk_SNhz9 Vids (on YouTube] 
 - [http://www.cs.toronto.edu/~hinton/ Hinton's homepage]

[https://www.youtube.com/playlist?list=PLE6Wd9FR--EfW8dtjAuPoTuPcqmOV53Fu Deep learning at Oxford 2015 (Nando de Freitas)]

[http://www.holehouse.org/mlclass/ Notes] for Andrew Ng's Coursera course.

== Papers == 
[http://cs229.stanford.edu/proj2013/TakeuchiLee-ApplyingDeepLearningToEnhanceMomentumTradingStrategiesInStocks.pdf Applying Deep Learning To Enhance Momentum Trading Strategies In Stocks]
 - [http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf Hinton (2010) -- A Practical Guide to Training Restricted Boltzmann Machines]
 - [https://www.cs.toronto.edu/~hinton/science.pdf Hinton, Salakhutdinov (2006) -- Reducing the Dimensionality of Data with Neural Networks]


== Books ==
[http://neuralnetworksanddeeplearning.com/index.html Nielsen -- Neural Networks and Deep Learning] <-- online book

[http://www.deeplearningbook.org/ http://www.deeplearningbook.org/]

== S/W ==
[http://playground.tensorflow.org http://playground.tensorflow.org]

[https://github.com/p-i-/SwiftNet SwiftNet] <-- My own back propagating NN (in Swift) 

== Misc ==
[http://www.oxford-man.ox.ac.uk/man Oxford AI/Trader]