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Deep Learning with R, Third Edition
From first principles to generative AI
Published by: Manning
Authors:
✅ François Chollet
o
https://www.manning.com/authors/francois-chollet
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https://www.linkedin.com/in/fchollet/
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Founder of Keras
§
https://keras.io/
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Co-Founder Ndea
§
https://ndea.com/
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Co-Founder ARC Prize
§
https://arcprize.org/
o
https://intro.co/francoischollet
o
https://fchollet.com/
✅ Tomasz Kalinowski
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https://www.manning.com/authors/tomasz-kalinowski
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https://www.linkedin.com/in/t-kalinowski/
o Engineering Manager, Posit (formerly RStudio)
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https://opensource.posit.co/people/tomasz-kalinowski/
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https://posit.co/
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https://github.com/t-kalinowski
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https://bsky.app/profile/t-kalinowski.bsky.social
Companion GitHub Repository:
Publication Date: June 2, 2026
Pages: 648
My Review Rating: 5-Stars
Also see my review on Amazon
My companion post on LinkedIn
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Review Title: A Master Class - A Deep Treatment of
Deep Learning with R
My immediate first impression of this book:
👉 In one word: Excellence;
👉 In three words: Attention to Detail.
I will admit upfront that I am a longtime fan of François
Chollet’s writing, and his creation of Keras (from Chapter 7, Section 7.1, “The
design of the Keras API is guided by the principle of progressive disclosure of
complexity: make it easy to get started, yet make it possible to handle
high-complexity use cases while requiring only incremental learning at each
step. Simple use cases should be easy and approachable, and arbitrarily
advanced workflows should be possible.”).
I consistently find great value in the books he has written.
This book is no “flash in the pan” – with 20
chapters, and over 600 pages of content.
This is a book that will extend your knowledge and help deepen
your understanding.
Beginning with ‘Chapter 2, The mathematical building blocks
of neural networks’, the authors set out to give you a foundation of
understanding. This type of foundational prep is missing in many books. Without
understanding the fundamental concepts and principles, the reader may be able
to do the mechanics of coding something – but will usually fall short in
understanding and be unaware/unable to apply the appropriate concepts, where/when
needed.
This is not a book that you should read quickly, nor is it a
book you should assume to read only once.
To get the optimum benefit of this book, you must put in the
work. Revisiting chapters, like an old friend.
You should approach reading this book, like you would if you
wanted to build muscles in your mind. There will be effort required to build
the skills that will develop intuition – and that’s what will likely
differentiate the diligent reader who selects this book, from the dilettante
that merely touches it with the fingertips.
The rewards will be many.
This book strikes an excellent balance between the narrative
of teaching, and the steady pacing of hands-on coding examples (which are
invariably well explained).
A key distinction that elevates this book above many others:
It teaches the Why, not just What.
Also, I suspect that readers will be pleased with the
quality of the illustrations, particularly those that immediately help
illustrate possibly unfamiliar concepts.
In some Japanese arts, there is the concept of a shokunin.
While it might be simplistically translated as “craftsman”, or “artisan” – the
meaning is much deeper: It represents a profound lifelong vocational philosophy
– a relentless, meditative drive to continuously refine their work. As
researchers, writers, and teachers – the authors are just such master shokunin.
A minor update that will be required for the next edition of this book:
re: See page-15 ("AI is making major strides toward helping accelerate science. The AlphaFold model from DeepMind is helping biologists predict protein structures with unprecedented accuracy.")
2026-07-28: Google DeepMind dismantles Nobel-winning AlphaFold team in strategy shift (Yahoo Finance > Financial Times)
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Additional Reading Suggestions:
- R in Action, Third Edition: Data analysis and graphics with R and Tidyverse (2022)
