Denoising and Variational Autoencoders

A video about autoencoders, a very powerful generative model. The video includes:
Intro: (0:25)
Dimensionality reduction (3:35)
Denoising autoencoders (10:50)
Variational autoencoders (18:15)
Training autoencoders (23:36)

Github repo: www.github.com/luisguiserrano/autoencoders

Recommended videos:
Generative adversarial networks:    • A Friendly Introduction to Generative...  
Restricted Boltzmann machines:    • Restricted Boltzmann Machines (RBM) -...  
Matrix factorization:    • How does Netflix recommend movies? Ma...  
Singular value decomposition:    • Singular Value Decomposition (SVD) an...  
Neural networks:    • A friendly introduction to Deep Learn...  
Convolutional neural networks:    • A friendly introduction to Convolutio...  
Recurrent neural networks:    • A friendly introduction to Convolutio...  
Logistic regression:    • Logistic Regression and the Perceptro...  
Shannon entropy:    • Shannon Entropy and Information Gain  

Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt

0:00 Introduction
0:13 Generative models
3:03 Variational autoencoders
3:45 Dataset of images
10:16 Denoising autoencoders
10:27 Linear methods
10:53 A friendly introduction to deep learning and neural networks
11:58 Mapping the real numbers to the interval (0,1)
12:23 Sigmoid function
12:41 Perceptron
15:02 Correct noise
18:20 Autoencoders as generators
20:16 Latent space
23:41 Training a neural network - loss function
25:18 Training an autoencoder
25:32 Training autoencoders
25:46 Reconstruction loss (Mean squared error)
26:31 Reconstruction loss (log-loss)
27:11 Training a variational auto encoder

Correction: At 30:05, the number in the middle of the red graph should be 0.4, not 0.3.