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

- A good **pattern for developing and training a GAN** includes the following steps. 

   1. **Preview** data from the real dataset. 

   2. **Test the discriminator** to see if it can at least learn to separate real data from random noise. 

   3. **Test the untrained generator** to see if it can create data of the right shape. 

   4. **Visualize the loss values** to understand how well training progressed. 

- A successfully trained GAN has a discriminator that can’t tell real from generated data. Its output is **0.5**, half way between **0.0** and **1.0**. The ideal mean squared error loss is **0.25**.

- It can be useful to **separately visualize the discriminator and generator loss**. The generator loss is the loss at the discriminator caused by a generated data example.

____

Summarize the GAN for generating a simple 1010 pattern .

📚Learning Points in Simple 1010 Pattern

✨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

📚Learning Points in Simple 1010 Pattern