Additional information
by Ronald T. Kneusel (Author)
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).
Author Biography
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.
Number of Pages: 584
Dimensions: 1.18 x 9.21 x 7.01 IN
Publication Date: July 08, 2025
