Learn Generative AI with PyTorch
Год издания: 2024
Автор: Liu M.
Издательство: Manning
ISBN: 978-1633436466
Язык: Английский
Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 434
Описание: 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.
Примеры страниц (скриншоты)
Оглавление
Part 1 Introduction to generative AI 1
1 What is generative AI and why PyTorch? 3
2 Deep learning with PyTorch 21
3 Generative adversarial networks: Shape and number generation 43
Part 2 Image generation 67
4 Image generation with generative adversarial networks 69
5 Selecting characteristics in generated images 97
6 CycleGAN: Converting blond hair to black hair 123
7 Image generation with variational autoencoders 142
Part 3 Natural language processing and Transformers 167
8 Text generation with recurrent neural networks 169
9 A line-by-line implementation of attention and Transformer 194
10 Training a Transformer to translate English to French 217
11 Building a generative pretrained Transformer from
12 Training a Transformer to generate text 264
Part 4 Applications and new developments 289
13 Music generation with MuseGAN 291
14 Building and training a music Transformer 318
15 Diffusion models and text-to-image Transformers 341
16 Pretrained large language models and the LangChain
appendix A Installing Python, Jupyter Notebook, and PyTorch 388
appendix B Minimally qualified readers and deep learning basics 395
index 401
