kaggle_username

kaggle_key

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

torch

J03T

J03O

J03P

J04AF

J04OF

J04N

J04CR

J05UCP

J07TMG

J07TTMG

J16TG

J16CGR

JAppebdixB

JAppebdixD

SPA04

SPA16

SPA13

SPA09

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

We have already discussed the Fashion MNIST dataset [here](https://www.educative.io/courses/make-your-first-gan-pytorch/the-fashion-mnist-dataset-class).

## Problem statement

A partial `Discriminator` class for the Fashion MNIST dataset is provided to you. Your task is to define the:
- Network architecture
  
   - The input layer should have `784+10` nodes.
   - The hidden layer 1 should have `200` nodes with the `LeakyReLU` activation applied to the output. 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`.

We have already discussed the Fashion MNIST dataset [here](https://www.educative.io/courses/make-your-first-gan-pytorch/the-fashion-mnist-dataset-class).

# Problem statement

A partial `Discriminator` class for the Fashion MNIST dataset is provided to you. Your task is to define the:
- Network architecture
  
   - The input layer should have `784+10` nodes.
   - The hidden layer 1 should have `200` nodes with the `LeakyReLU` activation applied to the output. 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`.

The task will test your knowledge on building the discriminator class for the conditional GAN.

🍀  Challenge: Define 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: Define the Discriminator

Problem statement