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Practical Deep Learning, 2nd Edition: A Python-Based Introduction
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
How neural networks work and how they’re trainedHow to use classical machine learning modelsHow to develop a deep learning model from scratchHow to evaluate models with industry-standard metricsHow 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 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).
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
How neural networks work and how they’re trainedHow to use classical machine learning modelsHow to develop a deep learning model from scratchHow to evaluate models with industry-standard metricsHow 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 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).
- GenresProgramming
583 pages, Kindle Edition
Published July 8, 2025
4 people are currently reading
6 people want to read
About the author
Ronald T. Kneusel
17 books30 followersMy infatuation with computers began in 1981 with an Apple II. I've been active in machine learning since 2003, and deep learning since before AlexNet was a thing.
My background includes a Ph.D. in computer science from the University of Colorado, Boulder (deep learning), and an M.S. in physics from Michigan State University.
By day, I work in industry building deep learning systems. By night, I type away on my keyboard generating the books you see here. I sincerely hope that if you explore my books, you gain as much enjoyment from them as I had in creating them.
Please see my full list of books here: www.rkneusel.com
Questions? Comments? Let me know at rkneuselbooks@gmail.com
My background includes a Ph.D. in computer science from the University of Colorado, Boulder (deep learning), and an M.S. in physics from Michigan State University.
By day, I work in industry building deep learning systems. By night, I type away on my keyboard generating the books you see here. I sincerely hope that if you explore my books, you gain as much enjoyment from them as I had in creating them.
Please see my full list of books here: www.rkneusel.com
Questions? Comments? Let me know at rkneuselbooks@gmail.com
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