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Make Your First GAN Using PyTorch

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✨Introduction

AI is Exploding!Who Is This Course For?

✨PyTorch Basics

Pytorch Tensors

Automatic Gradients with PyTorch

Computation Graphs

📚Learning Points in PyTorch Basics

🖥️ Project: Computation Graph and Gradient Calculation

🍀 Challenge: Setting Up a Computation Graph Solution Review: Setting Up a Computation Graph 🍀 Challenge: Finding Gradient Solution Review: Finding Gradient

✨First PyTorch Neural Network

MNIST Image Dataset Simple Neural Network Visualizing Training The MNIST Dataset Class Training Our Classifier Querying Our Neural Network Simple Classifier Performance 📚Learning Points in First PyTorch Neural Network

✨Refinements

Loss Function Activation Function Optimisation Function Normalisation Combined Refinements 📚Learning Points in Refinements

🖥️ Project: Fashion MNIST Classification Model

The Fashion MNIST Dataset Class 🍀 Challenge: Define the Neural Network Architecture Solution Review: Define the Neural Network Architecture The Fashion MNIST Classifier Performance

✨CUDA Basics

NumPy vs. Python Nvidia CUDA Using CUDA in Python 📚Learning Points in CUDA Basics

Course Assessment

Make Your First GAN: Part 1 - Exam

✨The GAN Idea

Generating Images Adversarial Training Training a GAN 📚Learning Points in The GAN Idea

✨Simple 1010 Pattern

Generating Real Data Building The Discriminator Training and Testing The Discriminator Building The Generator Training The 1010 Pattern GAN 📚Learning Points in Simple 1010 Pattern

🖥️ Project: A Simple 01110 Pattern GAN

Generating Real Data 🍀 Challenge: Building the Discriminator Solution Review: Building the Discriminator 🍀 Challenge: Building the Generator Solution Review: Building the Generator Training the 01110 Pattern GAN

✨Handwritten Digits

The MNIST Dataset Class The MNIST Discriminator The MNIST Generator Training the MNIST GAN Mode Collapse Improving GAN Training Experimenting with Seeds 📚Learning Points in Handwritten Digits

🖥️ Project: Fashion MNIST GAN

🍀 Challenge: Building the Discriminator Solution Review: Building the Discriminator 🍀 Challenge: Building the Generator Solution Review: Building the Generator Training The Fashion MNIST GAN

✨Human Faces

The Colour Images CelebA Dataset The Dataset Class The Discriminator The Generator Training The Human Faces GAN 📚Learning Points in Human Faces

Course Assessment

Make Your First GAN: Part 2 - Exam

✨Convolutional GANs

Introduction to Convolutional GANs MNIST CNN CelebA CNN Experimentation 📚Learning Points in Convolutional GANs

🖥️ Project: A Fashion MNIST CNN

🍀 Challenge: Define the Convolution Neural Network Architecture Solution Review: Define the Convolution Neural Network The Fashion MNIST CNN Classifier Performance

✨Conditional GANs

Introduction to Conditional GANs The Discriminator The Generator Training the MNIST cGAN Conditional GAN Results 📚Learning Points in Conditional GANs

🖥️ Project: Fashion MNIST Conditional GAN

🍀 Challenge: Define the Discriminator Solution Review: Define the Discriminator 🍀 Challenge: Define the Generator Solution Review: Define the Generator Training the Fashion MNIST cGAN

Course Assessment

Make Your First GAN: Part 3 - Exam

✨Conclusion

🏆 A Quick Overview 🚀 What's Next?

✨Appendix

Appendix 0: Author's Notes Appendix 1: Google Colab Workspace Appendix A: Ideal Loss Values Appendix B: GANs Learn Likelihood Appendix C: Convolution Examples Appendix D: Unstable Learning Appendix E: Acknowledgments

Computation Graphs

Learn about computation graphs: what they are and how we code them using PyTorch?

That automatic gradient calculation in the last section seems magical, but of course, it isn’t.

It is worth understanding just a little bit about how the automatic gradient calculation is done because this knowledge will help us later when we build larger networks.

Simple computation graph

Take a look at the following very simple network. It’s not a neural network, just a sequence of calculations.

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