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jaberg
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IPython Theano Tutorials

A collection of tutorials in ipynb format that illustrate how to do various things in Theano.

Theano Tutorials

  1. Introduction
  2. Simple computation
  3. Functions and Shared Variables
  4. Random Variables

Machine Learning Case Studies

  • Model - Logistic Regression with Theano.html

Other Stuff

  • Intro to Scikit Data (skdata).html
  • Preprocessing - Image Whitening.html
  • Notation for Machine Learning.html
  • Model - LIF Neurons with Theano.html

PyAutoDiff

  • Links to Related Work.html
  • Model - Autoencoders and Variations with PyAutodiff.html
  • Model - Convnet with PyAutodiff.html
  • Model - Linear SVM with PyAutodiff.html
  • Model - Multilayer Perceptron with PyAutodiff.html

Installation

Requirements:

  • numpy
  • scipy
  • matplotlib
  • IPython (>= 0.13)
  • theano
  • skdata (provides data sets for machine learning notebooks)
  • pyautodiff (required for some notebooks)

Instructions:

Download and unpack this project, and start up an ipython notebook to browse through the tutorials.

git clone https://github.com/jaberg/IPythonTheanoTutorials.git cd IPythonTheanoTutorials sh startipythonserver.sh

General

  • Theano Basics
  • Adding a custom Op to Theano
  • Numpy/Python function minimization using pyautodiff

Machine Learning:

Supervised Algorithms

  • Logistic Regression
  • Multilayer Perceptron (MLP)
  • Convolutional Network (Convnet)
  • Deep Belief Network (DBN)

Unsupervised Algorithms

  • Restricted Boltzmann Machine (RBM)
  • Autoassociator / Autoencoder (AA)
  • Stochasitc Denoising auto associator (SDAA)
  • Sparse coding

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