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ISBN10: 1633436462
ISBN13: 9781633436466
Publisher: Manning Publications
Published: Nov 26 2024
Pages: 432
Weight: 1.70
Height: 1.00 Width: 7.30 Depth: 9.20
Language: English
ISBN13: 9781633436466
Publisher: Manning Publications
Published: Nov 26 2024
Pages: 432
Weight: 1.70
Height: 1.00 Width: 7.30 Depth: 9.20
Language: English
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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology 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. About the book 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 About the reader Examples use simple Python. No deep learning experience required. About the author Mark Liu is the founding director of the Master of Science in Finance program at the University of Kentucky. The technical editor on this book was Emmanuel Maggiori. Table of Contents Part 1
1 What is generative AI and why PyTorch?
2 Deep learning with PyTorch
3 Generative adversarial networks: Shape and number generation
Part 2
4 Image generation with generative adversarial networks
5 Selecting characteristics in generated images
6 CycleGAN: Converting blond hair to black hair
7 Image generation with variational autoencoders
Part 3
8 Text generation with recurrent neural networks
9 A line-by-line implementation of attention and Transformer
10 Training a Transformer to translate English to French
11 Building a generative pretrained Transformer from scratch
12 Training a Transformer to generate text
Part 4
13 Music generation with MuseGAN
14 Building and training a music Transformer
15 Diffusion models and text-to-image Transformers
16 Pretrained large language models and the LangChain library
Appendixes
A Installing Python, Jupyter Notebook, and PyTorch
B Minimally qualified readers and deep learning basics
- 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. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology 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. About the book 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 About the reader Examples use simple Python. No deep learning experience required. About the author Mark Liu is the founding director of the Master of Science in Finance program at the University of Kentucky. The technical editor on this book was Emmanuel Maggiori. Table of Contents Part 1
1 What is generative AI and why PyTorch?
2 Deep learning with PyTorch
3 Generative adversarial networks: Shape and number generation
Part 2
4 Image generation with generative adversarial networks
5 Selecting characteristics in generated images
6 CycleGAN: Converting blond hair to black hair
7 Image generation with variational autoencoders
Part 3
8 Text generation with recurrent neural networks
9 A line-by-line implementation of attention and Transformer
10 Training a Transformer to translate English to French
11 Building a generative pretrained Transformer from scratch
12 Training a Transformer to generate text
Part 4
13 Music generation with MuseGAN
14 Building and training a music Transformer
15 Diffusion models and text-to-image Transformers
16 Pretrained large language models and the LangChain library
Appendixes
A Installing Python, Jupyter Notebook, and PyTorch
B Minimally qualified readers and deep learning basics
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