LECTURE 34: Autoencoders

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Presentation transcript:

LECTURE 34: Autoencoders Objectives: Autoencoders Stacked Autoencoders Denoising Autoencoders Resources: JB: Learning SU: Unsupervised Learning LP: Autoencoders LJ: Autoencoders PM: Autoencoders Keras: Building Autoencoders

Summary The autoencoding approach combined with a hierarchical network structure revolutionized deep learning systems. Stacked denoising autoencoders are very popular techniques for doing feature extraction, feature selection or dimensionality reduction. The denoising process can make training much more robust and avoid overfitting. http://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf