A Gentle Introduction to Generative Adversarial Networks (GANs)

What is GAN's main component and how does it work?

GANs have revolutionized the field of machine learning, enabling computers to generate incredibly realistic data, such as images, music, and even text. 

You might be wondering, “What exactly are GANs, and why should I care?” Well, GANs are a class of machine learning models designed to generate data that appears startlingly realistic. Whether it’s crafting lifelike images, composing compelling music, or even generating convincing text, GANs have the uncanny ability to simulate human-like creativity.

In this article, we aim to demystify GANs for you, our curious newsletter readers. We’ll start by unraveling the core concept of Generative Adversarial Networks, diving deep into their architecture, and exploring their primary components and how they work.

Whether you’re a novice dipping your toes into the AI waters or an experienced data scientist seeking a fresh perspective, this article is your gateway to understanding GANs inside and out.

A Gentle Introduction to Generative Adversarial Networks (GANs) / Image by Author 

Table of Contents:

1. What are Generative Adversarial Networks (GANs)?

2. GANs Architecture

3. Deep Convolution GANs (DCGANS)

4. The Discriminator Network

5. The Generator Network