Generative Ai – A Practical Approach

Generative AI with Auto Encoder, Generative Adverserial Network, Large Language Models etc…

What you'll learn

Understand the architecture and applications of autoencoders for data compression and reconstruction.

Explore GANs for generating realistic synthetic data across various domains.

Apply transformer models to solve sequence-based generative tasks effectively.

Integrate hybrid deep learning models for enhanced generative performance and flexibility.

Requirements

Python Programming, Machine Learning and Deep Learning

Description

This course offers a practical and in-depth exploration into the world of Generative AI, focusing on widely used and impactful models such as Autoencoders, Generative Adversarial Networks (GANs), and Large Language Models (LLMs). Designed for learners with a basic understanding of machine learning and Python, the course begins by introducing the fundamentals of generative modeling—how machines learn to create data that mimics real-world patterns.Students will first explore Autoencoders, including their vanilla and variational variants, and learn how to use them for tasks such as dimensionality reduction, anomaly detection, and data reconstruction. The course then transitions into GANs, diving into their unique adversarial training structure, generator-discriminator dynamics, and how they are used to create realistic images, audio, and other content.Next, learners will engage with transformers and LLMs, understanding how these models power modern tools like ChatGPT, enabling natural language generation, summarization, and creative writing. Each module includes hands-on coding exercises using popular deep learning frameworks to solidify theoretical concepts through real-world application.The course also addresses challenges such as training stability, ethical considerations, and model evaluation. Comparisons between generative approaches help students choose the right tool for specific tasks. By the end of the course, learners will be equipped to design, build, and apply generative AI models across various domains.

Overview

Section 1: Introduction

Lecture 1 Introduction to GenAI-1

Lecture 2 Introduction to GenAI-2

Section 2: Auto Encoders

Lecture 3 Introduction to Auto Encoder

Lecture 4 Vanilla Auto Encoder - Handson

Lecture 5 Regularized and Non-Regularized Autoencoders

Lecture 6 Variational Autoencoder

Lecture 7 Variational Autoencoder - Handson-1

Lecture 8 Variational Autoencoder - Handson-2

Lecture 9 Variational Autoencoder - Handson-3

This course is designed for data scientists, ML engineers, AI enthusiasts, and researchers who want hands-on experience with generative models like Autoencoders, GANs, and Transformers. A basic understanding of Python and deep learning is recommended.