PyTorch: Build, Evaluate, and Deploy Deep Learning Models

Rheinwerk Publishing, Inc · Bert Gollnick

Jul 2026 · Packt Publishing Ltd

Ebook

419

Pages

Tools

About this ebook

Build deep learning models with PyTorch for vision, recommendations, time series, and language tasks. Train, evaluate, fine-tune, monitor, and deploy models with modern supporting tools.Key Features

Broad model coverage spans vision, recommendations, graphs, forecasting, and language tasks
Production-focused workflows combine training metrics, monitoring, fine-tuning, and deployment
Modern PyTorch tooling includes Lightning, TensorBoard, MLflow, FastAPI, and Hugging Face tools

Book DescriptionDeep learning concepts are introduced alongside the PyTorch workflow needed to turn them into working models. Readers begin with model creation and progress through regression and classification, gaining the theoretical context required to understand evaluation tools such as confusion matrices and ROC curves. The middle of the journey expands into computer vision, recommendation systems, autoencoders, graph neural networks, time series forecasting, and language models. Hands-on exercises show how to create datasets, train networks, process sequential data, and generate images, while pretrained networks, Hugging Face fine-tuning, and PyTorch Lightning broaden the options for efficient development. The closing material focuses on training visibility and production use. MLflow and TensorBoard support logging, metric review, and monitoring, while FastAPI and Heroku illustrate deployment on local infrastructure or in the cloud. By the end of this journey, readers can build, tune, evaluate, and deploy PyTorch models across a wide range of practical deep learning tasks.What you will learn

Build neural networks with PyTorch
Train regression and classification models
Create computer vision and recommendation systems
Develop autoencoders and graph neural networks
Forecast time series and process language data
Evaluate, monitor, and deploy trained models

Who this book is for

Ideal for developers, machine learning engineers, data scientists, and research scientists who want practical PyTorch experience. Readers will benefit from an interest in building, evaluating, and deploying deep learning models across several application areas.

Build deep learning models with PyTorch for vision, recommendations, time series, and language tasks. Train, evaluate, fine-tune, monitor, and deploy models with modern supporting tools.Key Features Broad model coverage spans vision, recommendations, graphs, forecasting, and language tasks Produc...

Discover more

About the author

The Rheinwerk Computing series offers new and established professional comprehensive guidance to enrich the readers' skillsets and enhance their career prospects. Their publications are written by leading experts in the fields of programming, administration, security, analytics, and more. Each book is detailed and hands-on to help readers develop essential, practical skills that they can apply to their daily work.

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.

Rate this ebook

Tell us what you think.

Reading information

Smartphones and tablets

Install the Google Play Books app for Android and iPad/iPhone. It syncs automatically with your account and allows you to read online or offline wherever you are.

Laptops and computers

You can listen to audiobooks purchased on Google Play using your computer's web browser.

eReaders and other devices

To read on e-ink devices like Kobo eReaders, you'll need to download a file and transfer it to your device. Follow the detailed Help Center instructions to transfer the files to supported eReaders.

Flag as inappropriate