Unsupervised Feature Learning / Deep Learning Tutorial
- Modules are based on the Stanford UFLDL exercises (http://deeplearning.stanford.edu/tutorial/)
- General matrix structure follows the convention where rows indicate feature dimensions and columns indicate samples
- Generally, cost and gradient computations are separated in two separate functions due to external optimization library requirements
- In code documentation where algorithms are explained all formulas are vectorized (i.e. matrix operations) unless indices are used.
- Three debug levels are worth to notice: 0: no information is given out 1: warnings and messages are printed above 1: in addition to 1, figures are displayed
Modules and directories
- data : data used to test modules
- common : common modules for data read/write and visualization
- examples : examples illustrating networks constructed via combinations of different UL-SL methods
- Linreg : Linear regression
- Logreg : Logistic regression
- Softmax : Softmax regression
- SMNN : Supervised Multilayer Neural Network
- PCA : Principal Component Analysis
- ICA : Independent Component Analysis
- SoftICA : Independent Component Analysis with soft reconstruction constraint
- SparseAutoencoder : Sparse Autoencoder (Sigmoid and Linear)
- StackedAutoencoder : Stacked Autoencoder
- SparseCoding : Sparse Coding
- CNN : Convolutional Neural Network
Dependencies
- numpy (linear algebra)
- scipy (for optimization)
- pylab/matplotlib (for visualization)