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