Uploaded January 2022 | Updated September 2026, 2 weeks ago

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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: github.com/luisguiserrano/autoencoders

Recommended videos:
Generative adversarial networks: youtube.com/watch?v=8L11aMN5KY8
Restricted Boltzmann machines: youtube.com/watch?v=Fkw0_aAtwIw
Matrix factorization: youtube.com/watch?v=ZspR5PZemcs
Singular value decomposition: youtube.com/watch?v=DG7YTlGnCEo
Neural networks: youtube.com/watch?v=BR9h47Jtqyw
Convolutional neural networks: youtube.com/watch?v=2-Ol7ZB0MmU
Recurrent neural networks: youtube.com/watch?v=2-Ol7ZB0MmU
Logistic regression: youtube.com/watch?v=jbluHIgBmBo
Shannon entropy: youtube.com/watch?v=9r7FIXEAGvs

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.