Nipun Batra · Deep learning

Generative modelling,
one reasoning step at a time.

Start with fitting a distribution. Build a generative model, inspect its learning signal, and test what its samples actually show.

KL & variational inference → generative models, autoencoders and VAEs → GANs → diffusion
Slides lead. Short notebook demonstrations make the calculations visible.

01

143 min · 124 main slides + 28 optional · 331 reveal states

KL and variational inference

Bayes for a neural network, KL from scratch, and the ELBO

02

145 min · 103 main slides + 13 optional · 267 reveal states

From fitting a distribution to the VAE

Why generate, fit and sample, PCA and Gaussians on MNIST, autoencoders, and the variational autoencoder

03

61 min · 60 main slides + 3 optional · 120 reveal states

Generative adversarial networks

A learned critic supplies the learning signal

04

138 min · 68 main slides + 34 optional · 246 reveal states

Diffusion models

Create by learning to undo noise