Stanford Unsupervised Feature Learning and Deep Learning Tutorial

Tutorial Website: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial

Sparse Autoencoder

Sparse Autoencoder vectorized implementation, learning/visualizing features on MNIST data

load_MNIST.py: Load MNIST images
sample_images.py: Load sample images for testing sparse auto-encoder
gradient.py: Functions to compute & check cost and gradient
display_network.py: Display visualized features
sparse_autoencoder.py: Sparse autoencoder cost & gradient functions
train.py: Train sparse autoencoder with MNIST data and visualize learnt featured

Preprocessing: PCA & Whitening

Implement PCA, PCA whitening & ZCA whitening

pca_gen.py

Softmax Regression

Classify MNIST digits via softmax regression (multivariate logistic regression)

softmax.py: Softmax regression cost & gradient functions
softmax_exercise: Classify MNIST digits

Self-Taught Learning and Unsupervised Feature Learning

Classify MNIST digits via self-taught learning paradigm, i.e. learn features via sparse autoencoder using digits 5-9 as unlabelled examples and train softmax regression on digits 0-4 as labelled examples

stl_exercise.py: Classify MNIST digits via self-taught learning

Building Deep Networks for Classification (Stacked Sparse Autoencoder)

Stacked sparse autoencoder for MNIST digit classification

stacked_autoencoder.py: Stacked auto encoder cost & gradient functions
stacked_ae_exercise.py: Classify MNIST digits

Linear Decoders with Auto encoders

Learn features on 8x8 patches of 96x96 STL-10 color images via linear decoder (sparse autoencoder with linear activation function in output layer)

linear_decoder_exercise.py

Working with Large Images (Convolutional Neural Networks)

Classify 64x64 STL-10 images using features learnt via linear decoder (previous section) and convolutional neural networks

cnn.py: Convolution neural networks. Convolve & Pooling functions
cnn_exercise.py: Classify STL-10 images

License

jatinshah/ufldl_tutorial

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