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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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  • 14 Autoencoders
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14 AutoencodersΒΆ

  • 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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