Introduction to Generative AI

Course Overview

Beginner

Free Course

For developers and technical learners who are new to generative AI and want to know how these models actually work. You will be able to explain what a generative model learns, compare the main families (autoregressive transformers, VAEs, GANs, diffusion and flow models), and train a small one yourself.

Instructor: Jaidev

Sections: 3

Course Content

Section 1: Foundations of Generative Modeling

What a generative model learns, how it differs from a classifier, where it works in practice, and the four main ways such models are built.

1. What is Generative AI?

2. Discriminative vs Generative Models

3. Key Applications (text, image, music, code)

4. Overview of Generative Architectures

Section 2: Deep Dive into Architectures

How VAEs, GANs, diffusion models and transformers work inside, with the maths, the code and the failures to watch for.

1. Variational Autoencoders (VAEs)

2. Generative Adversarial Networks (GANs)

3. Diffusion Models

4. Transformers as Generative Models

Section 3: Mini Project

Build, train and diagnose a variational autoencoder that generates handwritten digits.

1. Mini Project: Train a Variational Autoencoder on MNIST