Difference between revisions of "Machine Learning"
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=== Boltzmann Machines === | === Boltzmann Machines === | ||
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| + | Boltzmann Distribution connects the probability of a system being in a certain state with the energy of that state: <math>P(s) \propto e^{-kE_s}</math> where <math>E_s</math> is the energy for that state. | ||
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| + | So we can write: <math>P(s) = \frac{1}{Z}e^{-kE_s}</math> where <math>Z</math> is the PARTITION FUNCTION (energy summed over all possible states). Reversing we can write <math>E_s = -\frac{1}{k} ln(Z P(s))</math> | ||
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| + | IIUC there is a wide class of systems that settle down into a Boltzmann distribution. | ||
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| + | Each (binary) vector of unit-outputs creates a corresponding Energy state. Let's assume these energy states form a Boltzmann distribution. | ||
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| + | We can calculate the Probability of a given unit's state being ON just by knowing the system's energy gap between that unit being ON and OFF: <math>p_\text{i=on} = \frac{1}{1+\exp(-\frac{\Delta E_i}{T})}</math> where <math>\Delta E_i = E_\text{i=off} - E_\text{i=on}</math> (working [https://en.wikipedia.org/wiki/Boltzmann_machine#Probability_of_a_unit.27s_state here]) | ||
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| + | ----- | ||
Think: Hopfield Net, but each neuron stochastically has state 0 or 1. Prob(state <- 1) = sigma(-z) where z = preactivation. | Think: Hopfield Net, but each neuron stochastically has state 0 or 1. Prob(state <- 1) = sigma(-z) where z = preactivation. | ||
Revision as of 15:31, 26 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
Boltzmann Machines
Boltzmann Distribution connects the probability of a system being in a certain state with the energy of that state: 
where 
is the energy for that state.
So we can write: 
where 
is the PARTITION FUNCTION (energy summed over all possible states). Reversing we can write 
IIUC there is a wide class of systems that settle down into a Boltzmann distribution.
Each (binary) vector of unit-outputs creates a corresponding Energy state. Let's assume these energy states form a Boltzmann distribution.
We can calculate the Probability of a given unit's state being ON just by knowing the system's energy gap between that unit being ON and OFF: 
where 
(working here)
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!
