Deep Learning with R, Third Edition

Nyhet

Inbunden, Engelska, 2026

699 kr

Kommande

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.

Produktinformation

Utgivningsdatum2026-03-04
Vikt744 g
FormatInbunden
SpråkEngelska
Antal sidor702
Upplaga3
FörlagPearson Education
ISBN9781633435186

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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

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