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Published in:
2018 | OriginalPaper | Chapter
8. Autoencoders
Author : Sandro Skansi
Published in: Introduction to Deep Learning
Publisher: Springer International Publishing
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Abstract
This chapter revisits the principal component analysis and the notion of distributed representations. The main focus here is on filling the part that was left out in Chap. 3, completing the exposition of the principal component analysis, and demonstrating what a distributed representation is in mathematical terms. The chapter then introduces the main unsupervised learning technique for deep learning, the autoencoder. The structural aspects are presented in detail with both explanations and illustrations, and several different types of autoencoders are presented as variations of a single theme. The idea of stacking autoencoders to produce even more condensed distributed representations is presented in detail, and the Python code for stacking and saving representations with autoencoders is provided with abundant explanations and illustrations. The chapter concludes with the recreation of a classical result in deep learning, an autoencoder that can learn to draw cats from watching unlabeled videos.