This repository is an attempt to translate the various demos contained in the theano deep learning tutorial to regular python. It was done for two reasons:
- To help me learn theano
- To validate my understanding of the content of the tutorials
I've also found that it is useful to benchmark theano against my implementations.
All benchmarks have been run on CPU not GPU.
Optimization complete with best validation score of 7.479167 %,with test performance 7.489583 % The code run for 75 epochs, with 1.674691 epochs/sec The code for file logistic_sgd.py ran for 44.8s
real 0m29.138s user 0m45.541s sys 0m2.177s
Optimization complete with best validation score of 7.479167 %,with test performance 7.489583 % The code run for 75 epochs, with 6.469746 epochs/sec The code for file logistic_sgd.py ran for 11.6s
real 0m9.328s user 0m14.780s sys 0m1.116s
Deep Learning Tutorials
Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.
The easiest way to follow the tutorials is to browse them online.
Main development of this project.
- code - Python files corresponding to each tutorial
- data - data and scripts to download data that is used by the tutorials
- doc - restructured text used by Sphinx to build the tutorial website
- html - built automatically by doc/Makefile, contains tutorial website
- issues_closed - issue tracking
- issues_open - issue tracking
- misc - administrative scripts
To build the html version of the tutorials, install sphinx and run doc/Makefile