19
Autoencoders
dlfc_notes
Preface
1
The Deep Learning Revolution
2
Probabilities
3
Standard distributions
4
Single layer networks: regression
5
Single layer networks: classification
6
Deep Neural Networks
7
Gradient Descent
8
Backpropagation
9
Regularisation
10
Convolutional networks
11
structured distributions
12
Transformers
13
Graph neural networks
14
Sampling
15
Discrete latent variables
16
Continuous latent variables
17
Generative adversarial networks
18
Normalizing Flows
19
Autoencoders
20
Diffusion Models
References
Table of contents
19.1
Deterministic autoencoders
19.1.1
Linear AEs
19.1.2
Deep AEs
19.1.3
Sparse AEs
19.1.4
Denoising AEs
19.1.5
Masked AEs
19.2
Variational autoencoders
19.2.1
Amortized inference
19.2.2
The reparameterization trick
19
Autoencoders
19.1
Deterministic autoencoders
19.1.1
Linear AEs
19.1.2
Deep AEs
19.1.3
Sparse AEs
19.1.4
Denoising AEs
19.1.5
Masked AEs
19.2
Variational autoencoders
19.2.1
Amortized inference
19.2.2
The reparameterization trick
18
Normalizing Flows
20
Diffusion Models