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Learn how generative AI works by building your very own models that can write coherent text, create realistic images, and even make lifelike music.
Learn Generative AI with PyTorch teaches the underlying mechanics of generative AI by building working AI models from scratch. Throughout, you’ll use the intuitive PyTorch framework that’s instantly familiar to anyone who’s worked with Python data tools. 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
The generative AI projects you create use the same underlying techniques and technologies as full-scale models like GPT-4 and Stable Diffusion. You don’t need to be a machine learning expert—you can get started with just some basic Python programming skills.
Transformers, Generative Adversarial Networks (GANs), diffusion models, LLMs, and other powerful deep learning patterns have radically changed the way we manipulate text, images, and sound. Generative AI may seem like magic at first, but with a little Python, the PyTorch framework, and some practice, you can build interesting and useful models that will train and run on your laptop. This book shows you how.
Learn Generative AI with PyTorch introduces the underlying mechanics of generative AI by helping you build your own working AI models. You’ll begin by creating simple images using a GAN, and then progress to writing a language translation transformer line-by-line. As you work through the fun and fascinating projects, you’ll train models to create anime images, write like Hemingway, make music like Mozart, and more. You just need Python and a few machine learning basics to get started. You’ll learn the rest as you go!
What’s inside
Build an English-to-French translator
Create a text-generation LLM
Train a diffusion model to produce high-resolution images
Music generators using GANs and Transformers
Table of Contents
Part 1. Introduction to generative AI
Chapter 1. What is generative AI and why PyTorch?
Chapter 2. Deep learning with PyTorch
Chapter 3. Generative adversarial networks: Shape and number generation
Part 2. Image generation
Chapter 4. Image generation with generative adversarial networks
Chapter 5. Selecting characteristics in generated images
Chapter 6. CycleGAN: Converting blond hair to black hair
Chapter 7. Image generation with variational autoencoders
Part 3. Natural language processing and Transformers
Chapter 8. Text generation with recurrent neural networks
Chapter 9. A line-by-line implementation of attention and Transformer
Chapter 10. Training a Transformer to translate English to French
Chapter 11. Building a generative pretrained Transformer from scratch
Chapter 12. Training a Transformer to generate text
Part 4. Applications and new developments
Chapter 13. Music generation with MuseGAN
Chapter 14. Building and training a music Transformer
Chapter 15. Diffusion models and text-to-image Transformers
Chapter 16. Pretrained large language models and the LangChain library
Appendix A. Installing Python, Jupyter Notebook, and PyTorch
Appendix B. Minimally qualified readers and deep learning basics
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