Chapter 18

Chapter 18 sections from Deep Learning with PyTorch.

6 items

Sections

  1. 18.1 Dimensionality Reduction
  2. 18.2 Sparse Autoencoders
  3. 18.3 Denoising Autoencoders
  4. 18.4 Variational Autoencoders
  5. 18.5 Latent Space Manipulation
  6. 18.6 Representation Learning
Writes › Book › Deep Learning with PyTorch › Part V › Chapter 18 ›

Dimensionality Reduction

Deep learning often begins with data that has many coordinates.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part V › Chapter 18 ›

Sparse Autoencoders

An ordinary autoencoder compresses information by forcing the latent representation to have fewer dimensions than the input.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part V › Chapter 18 ›

Denoising Autoencoders

A denoising autoencoder learns to recover a clean input from a corrupted version of that input.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part V › Chapter 18 ›

Variational Autoencoders

A variational autoencoder, or VAE, is a generative latent variable model trained with neural networks.
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part V › Chapter 18 ›

Latent Space Manipulation

Latent space manipulation studies how to change a learned representation $z$ in order to produce controlled changes in the decoded output. In an autoencoder, the encoder maps an input into a latent vector,
deep-learning pytorch
Writes › Book › Deep Learning with PyTorch › Part V › Chapter 18 ›

Representation Learning

Representation learning is the study of how models learn useful internal descriptions of data.
deep-learning pytorch