Deep Learning with PyTorch, Second Edition
2nd Edition
Published on 1. April 2026
Book
Hardback
544 pages
978-1-63343-885-9 (ISBN)
Description
Stop guessing at PyTorch syntax, start building production-ready models today. Bridge the gap between theory and working code with guided, hands-on projects. Confused by transformers and diffusion? Learn them through clear, incremental steps. Grow from basic tensors to complete neural networks without drowning in jargon. Feel confident diagnosing training issues using PyTorch's powerful visualization tools. Stay market-relevant by mastering the latest generative AI techniques right now.
Project-based learning: Build an end-to-end medical image classifier that cements every concept.
Flexible PyTorch APIs: Customize layers, losses, and optimizers for research or production speed.
CNNs, RNNs, Transformers: Apply the right architecture to vision, language, and multimodal tasks.
Generative models: Create text and images with large language models and diffusion networks.
Optimization know-how: Improve accuracy, reduce inference cost, and streamline model deployment.
Deep Learning with PyTorch, Second Edition, by Luca Antiga, Eli Stevens, Howard Huang, and Thomas Viehmann, delivers a credible, code-first roadmap for serious AI practitioners. The book guides you through every stage, from data loading to scaled deployment.
Each chapter introduces a single concept, then immediately applies it to a working project. Updated coverage of transformers, diffusion, and distributed training keeps the content current. Friendly explanations, annotated code, and ample visuals make complex ideas clear and actionable.
Finish the book able to design, train, and ship state-of-the-art models using PyTorch's flexible toolkit. You will upskill confidently and join the ranks of engineers pushing AI forward.
Ideal for Python developers, data scientists, and ML engineers seeking practical mastery of modern deep learning.
Project-based learning: Build an end-to-end medical image classifier that cements every concept.
Flexible PyTorch APIs: Customize layers, losses, and optimizers for research or production speed.
CNNs, RNNs, Transformers: Apply the right architecture to vision, language, and multimodal tasks.
Generative models: Create text and images with large language models and diffusion networks.
Optimization know-how: Improve accuracy, reduce inference cost, and streamline model deployment.
Deep Learning with PyTorch, Second Edition, by Luca Antiga, Eli Stevens, Howard Huang, and Thomas Viehmann, delivers a credible, code-first roadmap for serious AI practitioners. The book guides you through every stage, from data loading to scaled deployment.
Each chapter introduces a single concept, then immediately applies it to a working project. Updated coverage of transformers, diffusion, and distributed training keeps the content current. Friendly explanations, annotated code, and ample visuals make complex ideas clear and actionable.
Finish the book able to design, train, and ship state-of-the-art models using PyTorch's flexible toolkit. You will upskill confidently and join the ranks of engineers pushing AI forward.
Ideal for Python developers, data scientists, and ML engineers seeking practical mastery of modern deep learning.
Reviews / Votes
This book is an exceptional resource for anyone looking to dive into deep learning with the PyTorch framework. It masterfully introduces complex concepts in a way that is approachable for beginners and students, offering clear explanations and practical examples that make learning both engaging and effective.Raja Rao Budaraju, Senior Member of Technical Staff, Oracle
If you're looking for a way to quickly get up to speed in deep learning using PyTorch, I'd highly recommend this book. It'll provide you with a really solid base to expand and experiment with many different projects using PyTorch.
Jordan Samek, Data Analyst | Data Scientist
More details
Edition
2nd edition
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 186 mm
Thickness: 39 mm
Weight
732 gr
ISBN-13
978-1-63343-885-9 (9781633438859)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Luca Antiga | Eli Stevens | Howard Huang
Deep Learning with PyTorch, Second Edition
Training and applying deep learning and generative AI models
E-Book
03/2026
Simon + Schuster LLC
€43.51
Available for download
Person
Luca Antiga is a deep learning researcher and entrepreneur known for translating theory into high-impact AI applications. With extensive industry and academic collaborations, Luca brings clarity and pragmatic rigor to every page. He distills years of neural-network innovation into guidance that accelerates reader competence.
Eli Stevens is a seasoned machine-learning engineer recognized for simplifying complex architectures for production teams. With startup intensity and open-source spirit, Eli delivers frank, actionable insights throughout the book. He converts frontier research into approachable steps that help readers deploy real-world solutions.
Howard Huang is a software engineer on the core PyTorch team, known for advancing distributed training at scale. With insider knowledge of the framework, Howard injects authoritative best practices into the narrative. He turns deep infrastructure expertise into clear tactics that boost reader productivity.
Thomas Viehmann is a data-science consultant and educator praised for demystifying advanced AI concepts. With classroom experience and community mentorship, Thomas offers an encouraging, structured teaching style. He translates academic depth into tools and patterns readers can apply immediately.
Eli Stevens is a seasoned machine-learning engineer recognized for simplifying complex architectures for production teams. With startup intensity and open-source spirit, Eli delivers frank, actionable insights throughout the book. He converts frontier research into approachable steps that help readers deploy real-world solutions.
Howard Huang is a software engineer on the core PyTorch team, known for advancing distributed training at scale. With insider knowledge of the framework, Howard injects authoritative best practices into the narrative. He turns deep infrastructure expertise into clear tactics that boost reader productivity.
Thomas Viehmann is a data-science consultant and educator praised for demystifying advanced AI concepts. With classroom experience and community mentorship, Thomas offers an encouraging, structured teaching style. He translates academic depth into tools and patterns readers can apply immediately.
Content
PART 1: CORE PYTORCH
1 INTRODUCING DEEP LEARNING AND THE PYTORCH LIBRARY
2 PRETRAINED NETWORKS
3 IT STARTS WITH A TENSOR
4 REAL-WORLD DATA REPRESENTATION USING TENSORS
5 THE MECHANICS OF LEARNING
6 USING A NEURAL NETWORK TO FIT THE DATA
7 TELLING BIRDS FROM AIRPLANES: LEARNING FROM IMAGES
8 USING CONVOLUTIONS TO GENERALIZE
PART 2: PRACTICAL APPLICATIONS
9 HOW TRANSFORMERS WORK
10 DIFFUSION MODELS FOR IMAGES
11 USING PYTORCH TO FIGHT CANCER
12 COMBINING DATA SOURCES INTO A UNIFIED DATASET
13 TRAINING A CLASSIFICATION MODEL TO DETECT SUSPECTED TUMORS
14 IMPROVING TRAINING WITH METRICS AND AUGMENTATION
15 USING SEGMENTATION TO FIND SUSPECTED NODULES
16 TRAINING MODELS ON MULTIPLE GPUS
17 DEPLOYING TO PRODUCTION
1 INTRODUCING DEEP LEARNING AND THE PYTORCH LIBRARY
2 PRETRAINED NETWORKS
3 IT STARTS WITH A TENSOR
4 REAL-WORLD DATA REPRESENTATION USING TENSORS
5 THE MECHANICS OF LEARNING
6 USING A NEURAL NETWORK TO FIT THE DATA
7 TELLING BIRDS FROM AIRPLANES: LEARNING FROM IMAGES
8 USING CONVOLUTIONS TO GENERALIZE
PART 2: PRACTICAL APPLICATIONS
9 HOW TRANSFORMERS WORK
10 DIFFUSION MODELS FOR IMAGES
11 USING PYTORCH TO FIGHT CANCER
12 COMBINING DATA SOURCES INTO A UNIFIED DATASET
13 TRAINING A CLASSIFICATION MODEL TO DETECT SUSPECTED TUMORS
14 IMPROVING TRAINING WITH METRICS AND AUGMENTATION
15 USING SEGMENTATION TO FIND SUSPECTED NODULES
16 TRAINING MODELS ON MULTIPLE GPUS
17 DEPLOYING TO PRODUCTION