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 Adve... Restricted Boltzmann machines: • Generative model that won the 2024 Physics... Matrix factorization: • How does Netflix recommend movies? Matrix ... Singular value decomposition: • Singular Value Decomposition (SVD) and Ima... Neural networks: • A friendly introduction to Deep Learning a... Convolutional neural networks: • A friendly introduction to Convolutional N... Recurrent neural networks: • A friendly introduction to Convolutional N... Logistic regression: • Logistic Regression and the Perceptron Alg... 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.
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