Library Saved for Later Shopping Cart
Train and implement deep learning models with PyTorch!

PyTorch The Practical Guide

written by
$54.99

Available

$59.95

Available

$69.99

Available

Print edition E-book Bundle

$59.95

Available

$54.99

Available

$69.99

Available

415 pages, , Print edition paperback
ISBN 978-1-4932-2786-0 415 pages,
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2787-7 415 pages, , Print edition paperback
E-book formats: EPUB, PDF, online
ISBN 978-1-4932-2788-4
PyTorch is the framework for deep learning—so dive on in! Learn how to train, optimize, and deploy AI models with PyTorch by following practical exercises and example code. You’ll walk through using PyTorch for linear regression, classification, image processing, recommendation systems, autoencoders, graph neural networks, time series predictions, and language models—all the essentials. Then evaluate and deploy your models using key tools like MLflow, TensorBoard, and FastAPI. With information on fine-tuning your models using HuggingFace and reducing training time with PyTorch Lightning, this practical guide is the one you need!
  • Train, tune, and deploy deep learning models with PyTorch
  • Implement models for linear regression, classification, computer vision, recommendation systems, and more
  • Work with PyTorch Lightning, TensorBoard, LangChain, and FastAPI
About the E-book About the Book E-book in full color. PDF (30 MB) and EPUB (12 MB) files for download, DRM-free with personalized digital watermark. Copy and paste, bookmarks, and print-out permitted. Table of contents, in-text references, and index fully linked. Including online book edition in dedicated reader application. 415 pages, paperback, .84 in. Including code download. Reference book format 7 x 10 in. Printed black and white on 60# offset paper from sustainable sources. Reader-friendly serif font. One-column layout.

In this book, you'll learn about:

  1. Theory

    Get a thorough grounding in the concepts behind your models. Whether you’re looking to understand how a confusion matrix or ROC curve helps you evaluate a classification model or you want to grasp how recommendation system algorithms function, this guide has got you covered.

  2. Practice

    Move beyond theory with hands-on exercises and code. Create datasets for your linear regression models, use diffusion to create realistic images from noise, process sequential data with recurrent neural networks, and more.

  3. Deployment and Evaluation

    Monitor your training process, visualize metrics, and evaluate models with tools like MLflow and TensorBoard. Deploy models on-premise with FastAPI or in the cloud with Heroku.

Highlights include:

  • Deep learning
  • Linear regression
  • Classification
  • Computer vision
  • Recommendation systems
  • Autoencoders
  • Graph neural networks (GNNs)
  • Time series predictions
  • Language models
  • Pretrained networks
  • Evaluation and deployment
  • PyTorch Lightning

Bert Gollnick is a senior data scientist who specializes in renewable energies. For several years, he has taught courses about data science and machine learning, and more recently, about generative AI and natural language processing.

more >

Product supplements

Here you can find downloadable supplements for the product.

Supplements list >

You may also like: