Tutorial Website: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
Sparse Autoencoder vectorized implementation, learning/visualizing features on MNIST data
Implement PCA, PCA whitening & ZCA whitening
Classify MNIST digits via softmax regression (multivariate logistic regression)
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
Stacked sparse autoencoder for MNIST digit classification
Learn features on 8x8 patches of 96x96 STL-10 color images via linear decoder (sparse autoencoder with linear activation function in output layer)
Classify 64x64 STL-10 images using features learnt via linear decoder (previous section) and convolutional neural networks