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Contents:
Part I: Applied Math and Machine Learning Basics
Part II: Modern Practical Deep Networks
Part III: Deep Learning Research
14 Autoencoders
14.1 Undercomplete Autoencoders
14.3 Representational Power, Layer Size and Depth
14.4 Stochastic Encoders and Decoders
14.5 Denoising Autoencoders
14.6 Learning Manifolds with Autoencoder
14.7 Contractive Autoencoders
14.8 Predictive Sparse Decomposition
14.9 Applications of Autoencoders
15 Representation Learning
16 Structured Probablistic Models for Deep Learning
17 Monte Carlo Methods
18 Confronting the Partition Function
19 Approximate Inference
20 Deep Generative Models
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Part III: Deep Learning Research
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14 Autoencoders
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14 Autoencoders
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14.1 Undercomplete Autoencoders
14.3 Representational Power, Layer Size and Depth
14.4 Stochastic Encoders and Decoders
14.5 Denoising Autoencoders
14.6 Learning Manifolds with Autoencoder
14.7 Contractive Autoencoders
14.8 Predictive Sparse Decomposition
14.9 Applications of Autoencoders
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