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Create your own generative AI models for text, images, music, and more!

Generative AI tools like ChatGPT, Bard, and DALL-E have transformed the way we work. Learn Generative AI with PyTorch takes you on an incredible hands-on journey through creating and training AI models using Python, the free PyTorch framework and the hardware you already have in your office. Along the way, you’ll master the fundamentals of General Adversarial Networks (GANs), Transformers, Large Language Models (LLMs), variational autoencoders, diffusion models, LangChain, and more!

In Learn Generative AI with PyTorch you’ll build these amazing models:

A simple English-to-French translator
A text-generating model as powerful as GPT-2
A diffusion model that produces realistic flower images
Music generators using GANs and Transformers
An image style transfer model
A zero-shot know-it-all agent

All you need is Python and the fundamentals of machine learning to get started. You’ll learn the rest as you go!

Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications.

About the book

Learn Generative AI with PyTorch teaches the underlying mechanics of generative AI by building working AI models from scratch. Every model you’ll create is fun and fascinating, in projects that include generating color images of anime faces, changing the hair color in a photograph, training a model to write like Hemingway, and generating music in the style of Mozart.

Throughout, you’ll use the intuitive PyTorch framework that’s instantly familiar to anyone who’s worked with Python data tools. You’ll begin by creating simple content like shapes, numbers, and images using Generative Adversarial Networks (GANs). Then, each chapter introduces a new project as you work towards building your own LLMs.

About the reader

For Python programmers who know the basics of machine learning. No knowledge of PyTorch or generative AI required.

About the author

Dr. Mark Liu is a tenured finance professor and the founding director of the Master of Science in Finance program at the University of Kentucky. He has more than 20 years of coding experience, a Ph. D. in finance from Boston College.