  We implement stacked denoising autoencoders, a class of neural networks that
are capable of learning powerful representations of high dimensional data. We
describe stochastic gradient descent for unsupervised training of autoencoders,
as well as a novel genetic algorithm based approach that makes use of gradient
information. We analyze the performance of both optimization algorithms and
also the representation learning ability of the autoencoder when it is trained
on standard image classification datasets.
