Deep Learning with R, Third Edition

Tomasz Kalinowski (Autor)

Buch | Hardcover

625 Seiten

2026 | 3rd edition
Manning Publications (Verlag)
978-1-63343-518-6 (ISBN)

Noch nicht erschienen (ca. März 2026)
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For R programmers, the R interface to the Keras deep learning library is a powerful head start on building deep learning models without switching to Python. It provides a simple, consistent API that makes deep learning accessible and simplifies the process of building neural networks, even if you have no prior experience in advanced machine learning.

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 François 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.

François Chollet is the creator of Keras and a leading voice in practical deep learning. With global teaching experience, François 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.

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

Erscheint lt. Verlag	4.3.2026
Verlagsort	New York
Sprache	englisch
Themenwelt	Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
	Informatik ► Software Entwicklung ► Objektorientierung
	Informatik ► Software Entwicklung ► SOA / Web Services
	Informatik ► Theorie / Studium ► Algorithmen
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
	Mathematik / Informatik ► Informatik ► Web / Internet
	Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
ISBN-10	1-63343-518-0 / 1633435180
ISBN-13	978-1-63343-518-6 / 9781633435186
Zustand	Neuware