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Practical Deep Learning: A Python-Based Introduction
Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.
If you’ve been curious about 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 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
You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned.
The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
If you’ve been curious about 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 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
You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned.
The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.
464 pages, Paperback
Published February 23, 2021
About the author
Ronald T. Kneusel
18 books34 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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Displaying 1 - 2 of 2 reviews
March 1, 2022
Great Python book.
Another great book on Python that I would recommend.
This one does an amazing job at getting into some fairly deep machine learning modules and statistical models.
Would recommend for anyone in data science.
3.9/5
Another great book on Python that I would recommend.
This one does an amazing job at getting into some fairly deep machine learning modules and statistical models.
Would recommend for anyone in data science.
3.9/5
May 22, 2026
This is an interesting book chock full of insights regarding machine learning.
Yet, it had little about LLMs, which is what I read it for. Every aspect of LLMs is covered, but I do not think this book explained anything in a coherent way. Still, it had lots of interesting stuff in pieces--it just never coalesced.
Yet, it had little about LLMs, which is what I read it for. Every aspect of LLMs is covered, but I do not think this book explained anything in a coherent way. Still, it had lots of interesting stuff in pieces--it just never coalesced.
Displaying 1 - 2 of 2 reviews