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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](nbpages/Theano Tutorial Part 1 - Introduction.html)
  2. [Simple computation](nbpages/Theano Tutorial Part 2 - Simple Computation.html)
  3. [Functions and Shared Variables](nbpages/Theano Tutorial Part 3 - Functions and Shared Variables.html)
  4. [Random Variables](nbpages/Theano Tutorial Part 4 - Random Variables.html)

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 start_ipython_server.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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