1、Generative Adversarial Networks: A Comprehensive Guide

Generative Adversarial Networks: A Comprehensive Guide

1. Introduction

Generative Adversarial Networks (GANs) are a powerful concept in the field of machine learning. They offer a way to create new data that resembles a given dataset. This guide will walk you through various aspects of GANs, including their architecture, implementation, and practical applications.

2. Understanding GANs

• Generative and Discriminative Models : GANs consist of two main components - a generator and a discriminator. The generator tries to create new data, while the discriminator tries to distinguish between real and generated data.

  • How to Do It : To implement a basic GAN, you need to define the