Название: Deep Learning with PyTorch: Training and applying Deep Learning and Generative AI models, 2nd Edition (Final Release)
Автор: Howard Huang, Eli Stevens, Luca Antiga, Thomas Viehmann
Издательство: Manning Publications
Год: 2026
Страниц: 546
Язык: английский
Формат: pdf (true)
Размер: 28.8 MB

PyTorch core developer Howard Huang updates the bestselling original Deep Learning with PyTorch with new insights into the transformers architecture and generative AI models.

Instantly familiar to anyone who knows PyData tools like NumPy, PyTorch simplifies deep learning without sacrificing advanced features. In this book you’ll learn how to create your own neural network and deep learning systems and take full advantage of PyTorch’s built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. You’ll discover how easy PyTorch makes it to build your entire DL pipeline, including using the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. Each new technique you learn is put into action with practical code examples in each chapter, culminating into you building your own convolution neural networks, transformers, and even a real-world medical image classifier.

In Deep Learning with PyTorch, Second Edition you’ll find
• Deep learning fundamentals reinforced with hands-on projects
• Mastering PyTorch's flexible APIs for neural network development
• Implementing CNNs, transformers, and diffusion models
• Optimizing models for training and deployment
• Generative AI models to create images and text

About the technology
The powerful PyTorch library makes deep learning simple—without sacrificing the features you need to create efficient neural networks, LLMs, and other ML models. Pythonic by design, it’s instantly familiar to users of NumPy, Scikit-learn, and other ML frameworks. This thoroughly-revised second edition covers the latest PyTorch innovations, including how to create and refine generative AI models.

About the book
Deep Learning with PyTorch, Second Edition shows you how to build neural network models using the latest version of PyTorch. Clear explanations and practical projects help you master the fundamentals and explore advanced architectures including transformers and LLMs. Along the way you’ll learn techniques for training using augmented data, improving model architecture, and fine tuning.

What's inside
• PyTorch APIs for neural network development
• LLMs, transformers, and diffusion models
• Model training and deployment

About the reader
For Python programmers with a background in Machine Learning.

This book is intended for developers who are, or aim to become, Deep Learning practitioners and want to get acquainted with PyTorch. We imagine our typical reader to be a computer scientist, data scientist, or software engineer, or an undergraduate or graduate student in a related program. Since we don’t assume prior knowledge of Deep Learning, some parts in the first half of the book may be a repetition of concepts that are already understood by experienced practitioners. For those readers, we hope the exposition will provide a slightly different angle to known topics.

We expect readers to have basic knowledge of imperative and object-oriented programming. Since the book uses Python, you should be familiar with the syntax and operating environment. Knowing how to install Python packages and run scripts on your platform of choice is a prerequisite. Readers coming from C++, Java, jаvascript, Ruby, or other such languages should have an easy time picking it up, but they will need to do some catch-up outside this book. Similarly, being familiar with NumPy will be useful. We also expect familiarity with some basic linear algebra, such as knowing what matrices and vectors are and what a dot product is.

About the author
Howard Huang is a software engineer and developer on the PyTorch library focusing on large scale, distributed training. Eli Stevens, Luca Antiga, and Thomas Viehmann authored the first edition of Deep Learning with PyTorch.