64 lines
2.2 KiB
Markdown
64 lines
2.2 KiB
Markdown
|
[](https://bitdeli.com/free "Bitdeli Badge")
|
||
|
|
|
||
|
|
DeepLearnToolbox
|
||
|
|
================
|
||
|
|
|
||
|
|
A Matlab toolbox for Deep Learning.
|
||
|
|
|
||
|
|
Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data.
|
||
|
|
It is inspired by the human brain's apparent deep (layered, hierarchical) architecture.
|
||
|
|
A good overview of the theory of Deep Learning theory is
|
||
|
|
[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf)
|
||
|
|
|
||
|
|
For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng.
|
||
|
|
|
||
|
|
* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007)
|
||
|
|
* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010)
|
||
|
|
* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011)
|
||
|
|
|
||
|
|
If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284)
|
||
|
|
|
||
|
|
```
|
||
|
|
@MASTERSTHESIS\{IMM2012-06284,
|
||
|
|
author = "R. B. Palm",
|
||
|
|
title = "Prediction as a candidate for learning deep hierarchical models of data",
|
||
|
|
year = "2012",
|
||
|
|
}
|
||
|
|
```
|
||
|
|
|
||
|
|
Contact: rasmusbergpalm at gmail dot com
|
||
|
|
|
||
|
|
Directories included in the toolbox
|
||
|
|
-----------------------------------
|
||
|
|
|
||
|
|
`NN/` - A library for Feedforward Backpropagation Neural Networks
|
||
|
|
|
||
|
|
`CNN/` - A library for Convolutional Neural Networks
|
||
|
|
|
||
|
|
`DBN/` - A library for Deep Belief Networks
|
||
|
|
|
||
|
|
`SAE/` - A library for Stacked Auto-Encoders
|
||
|
|
|
||
|
|
`CAE/` - A library for Convolutional Auto-Encoders
|
||
|
|
|
||
|
|
`util/` - Utility functions used by the libraries
|
||
|
|
|
||
|
|
`data/` - Data used by the examples
|
||
|
|
|
||
|
|
`tests/` - unit tests to verify toolbox is working
|
||
|
|
|
||
|
|
For references on each library check REFS.md
|
||
|
|
|
||
|
|
Setup
|
||
|
|
-----
|
||
|
|
|
||
|
|
1. Download.
|
||
|
|
2. addpath(genpath('DeepLearnToolbox'));
|
||
|
|
|
||
|
|
Known errors
|
||
|
|
------------------------------
|
||
|
|
|
||
|
|
`test_cnn_gradients_are_numerically_correct` fails on Octave because of a bug in Octave's convn implementation. See http://savannah.gnu.org/bugs/?39314
|
||
|
|
|
||
|
|
`test_example_CNN` fails in Octave for the same reason.
|