Practical Deep Learning, 2nd Edition

Ronald T. Kneusel

ISBN 10: 1718504209 / ISBN 13: 9781718504202

Published by No Starch Press,US, US, 2025

About this Item

If you've been curious about artificial intelligence and machine learning but didn't know where to start, this is the book you've been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning, 2nd Edition teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math - the book will cover the rest. After an introduction to Python, you'll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models' performance. You'll also learn: How to use classic machine learning models like k-Nearest Neighbours, Random Forests, and Support Vector Machines, How neural networks work and how they're trained, How to use convolutional neural networks, How to develop a successful deep learning model from scratch. You'll conduct experiments along the way, building to a final case study that incorporates everything you've learned. This second edition is thoroughly revised and updated, and adds six new chapters to further your exploration of deep learning from basic CNNs to more advanced models. New chapters cover fine tuning, transfer learning, object detection, semantic segmentation, multilabel classification, self-supervised learning, generative adversarial networks, and large language models. The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning, 2nd Edition will give you the skills and confidence to dive into your own machine learning projects.

Seller Inventory # LU-9781718504202

Bibliographic Details

• Title: Practical Deep Learning, 2nd Edition
• Publisher: No Starch Press,US, US
• Publication Date: 2025
• Language: English
• ISBN 10: 1718504209
• ISBN 13: 9781718504202
• Binding: Paperback
• Edition: 2nd Edition
• Condition: New

About this title

Synopsis

Deep learning made simple.

Dip into deep learning without drowning in theory with this fully updated edition of Practical Deep Learning from experienced author and AI expert Ronald T. Kneusel.

After a brief review of basic math and coding principles, you’ll dive into hands-on experiments and learn to build working models for everything from image analysis to creative writing, and gain a thorough understanding of how each technique works under the hood. Whether you’re a developer looking to add AI to your toolkit or a student seeking practical machine learning skills, this book will teach you:

• How neural networks work and how they’re trained
• How to use classical machine learning models
• How to develop a deep learning model from scratch
• How to evaluate models with industry-standard metrics
• How to create your own generative AI models

Each chapter emphasizes practical skill development and experimentation, building to a case study that incorporates everything you’ve learned to classify audio recordings. Examples of working code you can easily run and modify are provided, and all code is freely available on GitHub. With Practical Deep Learning, second edition, you’ll gain the skills and confidence you need to build real AI systems that solve real problems.

New to this edition: Material on computer vision, fine-tuning and transfer learning, localization, self-supervised learning, generative AI for novel image creation, and large language models for in-context learning, semantic search, and retrieval-augmented generation (RAG).

About the Author

Ronald T. Kneusel earned a PhD in machine learning from the University of Colorado, Boulder, and has over 20 years of machine learning experience in industry. Kneusel is also the author of numerous books, including Math for Programming (2025), The Art of Randomness (2024), How AI Works (2023), Strange Code (2022), and Math for Deep Learning (2021), all from No Starch Press.