Sparse autoencoder Lecture Notes by Andrew Ng

http://www.stanford.edu/class/cs294a/sparseAutoencoder.pdf


These notes describe the sparse autoencoder learning algorithm, which
is one approach to automatically learn features from unlabeled data. In some
domains, such as computer vision, this approach is not by itself competitive
with the best hand-engineered features, but the features it can learn do turn
out to be useful for a range of problems (including ones in audio, text, etc).

Further, there're more sophisticated versions of the sparse autoencoder (not
described in these notes, but that you'll hear more about later in the class)
that do surprisingly well, and in many cases are competitive with or superior
to even the best hand-engineered representations.