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Full 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 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.
Contents
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
