Get Discount 10% Off
Subscribe to our newsletters now and stay up-to-date with new arrivals, updates and deals.
MILLIONS OF TITLES TO CHOOSE FROM
Currency
Deep Learning with Pytorch, Second Edition: Training and Applying Deep Learning and Generative AI Models
Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook...
- $99.98 USD
$177.99 USD- $99.98 USD
- Unit price
- per
Please hurry! Only 21 left in stock
Couldn't load pickup availability
Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book. 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. 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. Table of Contents Part 1
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
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 GPU
Author: Luca Antiga,Eli Stevens,Howard Huang
Publisher: Manning Publications
Published: 03/10/2026
Pages: 544
Binding Type: Paperback
Weight: 1.65lbs
Size: 9.27h x 7.43w x 1.47d
ISBN: 9781633438859
About the Author
Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Howard Huang is a software engineer and developer on the PyTorch library. During his tenure at PyTorch he has focused on large scale, distributed training. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer.
- 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. 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. Table of Contents Part 1
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
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 GPU
Author: Luca Antiga,Eli Stevens,Howard Huang
Publisher: Manning Publications
Published: 03/10/2026
Pages: 544
Binding Type: Paperback
Weight: 1.65lbs
Size: 9.27h x 7.43w x 1.47d
ISBN: 9781633438859
About the Author
Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Howard Huang is a software engineer and developer on the PyTorch library. During his tenure at PyTorch he has focused on large scale, distributed training. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer.
Returns Policy
You may return most new, unopened items within 30 days of delivery for a full refund. We'll also pay the return shipping costs if the return is a result of our error (you received an incorrect or defective item, etc.).
You should expect to receive your refund within four weeks of giving your package to the return shipper, however, in many cases you will receive a refund more quickly. This time period includes the transit time for us to receive your return from the shipper (5 to 10 business days), the time it takes us to process your return once we receive it (3 to 5 business days), and the time it takes your bank to process our refund request (5 to 10 business days).
If you need to return an item, simply login to your account, view the order using the "Complete Orders" link under the My Account menu and click the Return Item(s) button. We'll notify you via e-mail of your refund once we've received and processed the returned item.
Shipping
We can ship to virtually any address in the world. Note that there are restrictions on some products, and some products cannot be shipped to international destinations.
When you place an order, we will estimate shipping and delivery dates for you based on the availability of your items and the shipping options you choose. Depending on the shipping provider you choose, shipping date estimates may appear on the shipping quotes page.
Please also note that the shipping rates for many items we sell are weight-based. The weight of any such item can be found on its detail page. To reflect the policies of the shipping companies we use, all weights will be rounded up to the next full pound.
- Choosing a selection results in a full page refresh.