Product guide

Mastering PyTorch, 2nd Edition: Deep Learning with PyTorch

Mastering PyTorch is a 2nd edition guide to building and deploying deep learning models, spanning CNNs, transformers, generative models, and PyTorch tools. It targets readers with working knowledge of deep learning with Python, rather than complete beginners.

At a glance

Covers CNNs, recurrent networks, transformers, graph neural networks, diffusion and generative models, and deep reinforcement learning.
Describes training optimization with multiple CPUs and GPUs, mixed precision, and production and mobile deployment.
Includes material on Hugging Face, fastai, PyTorch Lightning, AutoML, explainable AI, recommendation systems, Flask, and Docker.

Price

$34.58Save 33%

Reference price: ~~$51.99~~

Price checked: Nov 14, 2026

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Pros and cons

Pros

Broad range of model types and application topics, from image and language tasks to music generation and reinforcement learning.
Connects model development with training optimization, deployment, and tools from the PyTorch ecosystem.
Customer feedback specifically notes in-depth explanations, code examples, and a reported GitHub code repository.

Cons

The listing says working knowledge of deep learning with Python is required, so it is not positioned as a first programming or deep-learning text.

Technical specifications

9 specifications

Publication
Publisher	Packt Publishing
Publication date	May 31, 2024
Edition	2nd ed.
Language	English
Print length	558 pages
ISBN-10	1801074305
ISBN-13	978-1801074308

Dimensions and installation
Weight listed	3.53 ounces
Dimensions	7.5 x 1.25 x 9.25 inches

Who is it for?

A good fit for

Data scientists and machine-learning engineers who want an advanced, PyTorch-centered reference.
Deep-learning practitioners and researchers exploring topics such as diffusion, graph neural networks, Hugging Face, or model deployment.
Readers switching from TensorFlow who already have working knowledge of deep learning with Python.

Less suited to

Readers seeking an introductory programming or deep-learning book with no prior Python background.

Customer reviews on Amazon

4.2 / 5 · 62 ratings

5 stars69%69%
4 stars12%12%
3 stars3%3%
2 stars8%8%
1 star8%8%

Customer feedback describes the book as covering both fundamentals and advanced topics, with detailed explanations and code snippets illustrating models. One customer notes that code is accessible through a GitHub repository. This reported breadth may suit readers building on existing Python and deep-learning knowledge, while the ratings distribution is not uniformly positive, so feedback alone cannot establish that the explanations or code will suit every reader.

Positive themes

Customers report broad coverage that includes both basics and advanced topics.
Feedback mentions in-depth explanations and code snippets for models.
One customer notes convenient access to code through a GitHub repository.

Frequently asked questions

Who is this book aimed at?

The listing names data scientists, machine-learning engineers and researchers, and deep-learning practitioners. It requires working knowledge of deep learning with Python.

What subjects does the book cover?

Listed topics include CNNs, recurrent networks, transformers, graph neural networks, generative and diffusion models, reinforcement learning, training optimization, and deployment.

Does it cover tools beyond core PyTorch?

Yes. The description names Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, Docker, AutoML, Captum, and TorchRec.

What edition and length are listed?

The listing identifies this as the 2nd edition, published May 31, 2024, with a print length of 558 pages.