Pytorch: The Practical Guide

Author:   Bert Gollnick
Publisher:   Rheinwerk Computing
Edition:   New edition
ISBN:  

9781493227860


Pages:   425
Publication Date:   25 March 2026
Format:   Paperback
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

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Pytorch: The Practical Guide


Overview

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 learning 2) Linear regression 3) Classification 4) Computer vision 5) Recommendation systems 6) Autoencoders 7) Graph neural networks (GNNs) 8) Time series predictions 9) Language models 10) Pretrained networks 11) Evaluation and deployment 12) PyTorch Lightning

Full Product Details

Author:   Bert Gollnick
Publisher:   Rheinwerk Computing
Imprint:   Rheinwerk Computing
Edition:   New edition
ISBN:  

9781493227860


ISBN 10:   1493227866
Pages:   425
Publication Date:   25 March 2026
Audience:   General/trade ,  General
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

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Author Information

Bert Gollnick is a senior data scientist, specializing in renewable energies. For many 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.

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