PyTorch : The Practical Guide

Bert Gollnick

ISBN 10: 1493227866 / ISBN 13: 9781493227860

Published by Rheinwerk Verlag Gmbh Mär 2026, 2026

About this Item

Neuware - 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!Highlights:1) Deep learning2) Linear regression3) Classification4) Computer vision5) Recommendation systems6) Autoencoders7) Graph neural networks (GNNs)8) Time series predictions9) Language models10) Pretrained networks11)Evaluation and deployment12)PyTorch Lightning.

Seller Inventory # 9781493227860

Bibliographic Details

• Title: PyTorch : The Practical Guide
• Publisher: Rheinwerk Verlag Gmbh Mär 2026
• Publication Date: 2026
• Language: English
• ISBN 10: 1493227866
• ISBN 13: 9781493227860
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 254W x 178H x 0D millimeters 254 millimeters width by 178 millimeters height by 0 millimeters depth

About this title

Synopsis

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

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.

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.

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.

About the Author

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. Bert studied aeronautics at the Technical University of Berlin and economics at the University of Hagen. His main areas of interest are machine learning and data science.