
Deep Learning with R, Third Edition
From First Principles to Generative AI
Tomasz Kalinowski(Author)
Manning Publications (Publisher)
3rd Edition
Will be published approx. on 6. June 2026
Book
Paperback/Softback
648 pages
978-1-63343-518-6 (ISBN)
Description
Ready to bring R code into the AI era? Stop switching languages. Build deep learning models in pure R. Master GPT-style transformers and diffusion. Skip complex math. Launch production-ready solutions confidently.
Keras 3 interface: Code modern neural networks with the simplicity R users love.
Vision, text, and time series: Apply models that classify images, translate text, and predict demand.
Transformers and LLMs: Generate fluent language and summaries without Python detours.
Diffusion imagery: Create new pictures and explore generative art inside RStudio.
Scaling and tuning: Fine-tune hyperparameters for faster training and top-tier accuracy.
Interpretability tools: Explain model decisions to bosses, regulators, and stakeholders.
Deep Learning with R, Third Edition pairs Keras creator Francois Chollet with R expert Tomasz Kalinowski to deliver an authoritative guide.
Step-by-step chapters move from first principles to advanced projects. Clear code, concise explanations, and runnable notebooks keep learning practical. New coverage of transformers, diffusion, and GPT-style language models brings bleeding-edge AI to R.
By book's end, you will design, train, and deploy high-performing models, interpret their outputs, and scale them for production. Your R workflow becomes an AI powerhouse.
Ideal for data scientists and analysts with intermediate R skills who crave modern deep learning capabilities.
Keras 3 interface: Code modern neural networks with the simplicity R users love.
Vision, text, and time series: Apply models that classify images, translate text, and predict demand.
Transformers and LLMs: Generate fluent language and summaries without Python detours.
Diffusion imagery: Create new pictures and explore generative art inside RStudio.
Scaling and tuning: Fine-tune hyperparameters for faster training and top-tier accuracy.
Interpretability tools: Explain model decisions to bosses, regulators, and stakeholders.
Deep Learning with R, Third Edition pairs Keras creator Francois Chollet with R expert Tomasz Kalinowski to deliver an authoritative guide.
Step-by-step chapters move from first principles to advanced projects. Clear code, concise explanations, and runnable notebooks keep learning practical. New coverage of transformers, diffusion, and GPT-style language models brings bleeding-edge AI to R.
By book's end, you will design, train, and deploy high-performing models, interpret their outputs, and scale them for production. Your R workflow becomes an AI powerhouse.
Ideal for data scientists and analysts with intermediate R skills who crave modern deep learning capabilities.
Reviews / Votes
The writing is excellent: theoretical concepts introduced (without math) very adequately, balanced mix between "theory" and practice, well-chosen examples, realistic use cases. In a nutshell, this is one of the best writing I have reviewed (or read) over the last 5 years at Manning.Alain M. Couniot, Senior Enterprise Architect, Sopra Steria Benelux
A fully updated classic in Deep Learning with the latest trends in Deep Learning including GPTs and image generation.
Juan Delgado, Data Analyst, Sodexo BRS
More details
Edition
3rd edition
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 234 mm
Width: 186 mm
Thickness: 48 mm
Weight
771 gr
ISBN-13
978-1-63343-518-6 (9781633435186)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

François Chollet | Tomasz Kalinowski
Deep Learning with R, Third Edition
From first principles to generative AI
E-Book
approx. 06/2026
Simon + Schuster LLC
€57.35
Not yet available
Person
Francois Chollet is the creator of Keras and a leading voice in practical deep learning. With global teaching experience, Francois delivers clarity and rigor on every page. He distills cutting-edge research into approachable lessons that help readers build real models fast. ?
Tomasz Kalinowski is a software engineer at Posit, maintaining the Keras and TensorFlow R packages. Drawing on years of community support, Tomasz writes with empathy and hands-on insight. He translates complex APIs into smooth R workflows that empower readers to innovate.
Tomasz Kalinowski is a software engineer at Posit, maintaining the Keras and TensorFlow R packages. Drawing on years of community support, Tomasz writes with empathy and hands-on insight. He translates complex APIs into smooth R workflows that empower readers to innovate.
Content
1 WHAT IS DEEP LEARNING?
2 THE MATHEMATICAL BUILDING BLOCKS OF NEURAL NETWORKS
3 INTRODUCTION TO TENSORFLOW, PYTORCH, JAX, AND KERAS
4 CLASSIFICATION AND REGRESSION
5 FUNDAMENTALS OF MACHINE LEARNING
6 THE UNIVERSAL WORKFLOW OF MACHINE LEARNING
7 A DEEP DIVE ON KERAS
8 IMAGE CLASSIFICATION
9 CONVNET ARCHITECTURE PATTERNS
10 INTERPRETING WHAT CONVNETS LEARN
11 IMAGE SEGMENTATION
12 OBJECT DETECTION
13 TIMESERIES FORECASTING
14 TEXT CLASSIFICATION
15 LANGUAGE MODELS AND THE TRANSFORMER
16 TEXT GENERATION
17 IMAGE GENERATION
18 BEST PRACTICES FOR THE REAL WORLD
19 THE FUTURE OF AI
2 THE MATHEMATICAL BUILDING BLOCKS OF NEURAL NETWORKS
3 INTRODUCTION TO TENSORFLOW, PYTORCH, JAX, AND KERAS
4 CLASSIFICATION AND REGRESSION
5 FUNDAMENTALS OF MACHINE LEARNING
6 THE UNIVERSAL WORKFLOW OF MACHINE LEARNING
7 A DEEP DIVE ON KERAS
8 IMAGE CLASSIFICATION
9 CONVNET ARCHITECTURE PATTERNS
10 INTERPRETING WHAT CONVNETS LEARN
11 IMAGE SEGMENTATION
12 OBJECT DETECTION
13 TIMESERIES FORECASTING
14 TEXT CLASSIFICATION
15 LANGUAGE MODELS AND THE TRANSFORMER
16 TEXT GENERATION
17 IMAGE GENERATION
18 BEST PRACTICES FOR THE REAL WORLD
19 THE FUTURE OF AI