L18.5: Tips and Tricks to Make GANs Work @SebastianRaschka
L18.5: Tips and Tricks to Make GANs Work  @SebastianRaschka
Uploaded April 2021 | Updated September 2026, 2 weeks ago
Sebastian's books: sebastianraschka.com/books

Slides: sebastianraschka.com/pdf/lecture-notes/stat453ss21/L18_gan__slides.pdf

Code: github.com/rasbt/stat453-deep-learning-ss21/tree/main/L18
GAN Tips repo: github.com/soumith/ganhacks

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This video is part of my Introduction of Deep Learning course.

Next video: youtu.be/5fs9PMzrVig

The complete playlist: youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51

A handy overview page with links to the materials: sebastianraschka.com/blog/2021/dl-course.html

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L18.5: Tips and Tricks to Make GANs WorkL8.8 Softmax Regression Derivatives for Gradient DescentL17.5 A Variational Autoencoder for Handwritten Digits in PyTorch   Code ExampleL5.1 Online, Batch, and Minibatch ModeL12.5 Choosing Different Optimizers in PyTorchL9.3.3 Multilayer Perceptron in PyTorch   Code Example Part 3/3 (Script Setup)L18.4: A GAN for Generating Handwritten Digits in PyTorch   Code ExampleL3.0 Perceptron Lecture OverviewL12.4 Adam: Combining Adaptive Learning Rates and MomentumL19.6 DistilBert Movie Review Classifier in PyTorch   Code ExampleL19.1 Sequence Generation with Word and Character RNNsL12.3 SGD with Momentum
Sebastian Raschka |

L18.5: Tips and Tricks to Make GANs Work

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