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Gain insights into Generative Adversarial Networks with PyTorch. Delve into neural networks, adversarial training, and building GANs for image generation, including conditional GANs for class-specific data.

GAN.tar.gz

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This course is an introduction to Generative Adversarial Networks (GANs) and a practical step-by-step tutorial on making your own with PyTorch. Through this course, you will learn how to build GANs with industry-standard tools.

In the first section, you will dive into PyTorch and refresh your understanding of neural networks by building a simple image classifier. In the second section of this course, you will explore the idea of adversarial training and build progressively more sophisticated GANs; first by learning a simple 1010 pattern, then monochrome images of handwritten digits, and finally full-color images of faces.

The third, and last section, extends the core GAN concept, applying it to convolutional neural networks, and developing a conditional GAN for generating data of the desired class.

By the end of this course, you will have a solid understanding of how to build GANs for your machine learning projects.

Make Your First GAN Using PyTorch

## Problem statement

A partial `Discriminator` class is provided to you. Your task is to define the:
- Network architecture
  
   - The input layer should have `784` nodes.
   - The hidden layer should have `200` nodes with the `LeakyReLU` activation applied to the output. The gradient of the left half of the function is $0.02$. 
The hidden layer output should be normalized.
   - The output layer should have `1` node with the `Sigmoid` activation function applied to the output.
      
   
-  Loss function as `BCE`.
-  Optimiser as `Adam`. The learning rate should be `0.0001`.

# Problem statement

A partial `Discriminator` class is provided to you. Your task is to define the:
- Network architecture
  
   - The input layer should have `784` nodes.
   - The hidden layer should have `200` nodes with the `LeakyReLU` activation applied to the output. The gradient of the left half of the function is $0.02$. 
The hidden layer output should be normalized.
   - The output layer should have `1` node with the `Sigmoid` activation function applied to the output.
      
   
-  Loss function as `BCE`.
-  Optimiser as `Adam`. The learning rate should be `0.0001`.

This task will test your knowledge on building the Fashion MNIST discriminator class.

🍀 Challenge: Building the Discriminator

✨Introduction

✨PyTorch Basics

🖥️ Project: Computation Graph and Gradient Calculation

✨First PyTorch Neural Network

✨Refinements

🖥️ Project: Fashion MNIST Classification Model

✨CUDA Basics

Make Your First GAN: Part 1 - Exam

✨The GAN Idea

✨Simple 1010 Pattern

🖥️ Project: A Simple 01110 Pattern GAN

✨Handwritten Digits

🖥️ Project: Fashion MNIST GAN

✨Human Faces

Make Your First GAN: Part 2 - Exam

✨Convolutional GANs

🖥️ Project: A Fashion MNIST CNN

✨Conditional GANs

🖥️ Project: Fashion MNIST Conditional GAN

Make Your First GAN: Part 3 - Exam

✨Conclusion

✨Appendix

🍀 Challenge: Building the Discriminator

Problem statement