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229610421
Deep Learning with R, Third Edition: From first principles to generative AI
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229610421
SCR 1455
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- Get the eBook free when you register your print book at Manning.This book introduces deep learning from scratch with examples that use the R language and the Keras library. Each chapter offers practical code examples that build your understanding of deep learning layer by layer. You’ll appreciate the intuitive explanations, crisp illustrations, and clear examples. In this expanded third edition you’ll find fresh chapters on the transformers architecture, building your own GPT-like large language model, and image generation with diffusion models. Plus, even DL veterans will benefit from the insightful explanations on the nature of deep learning.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.In Deep Learning with R, Third Edition you will learn:• Deep learning from first principles• The latest features of Keras• Image classification and image segmentation• Time series forecasting• Text classification and machine translation• Text and image generation—build your own LLMs and diffusion models!• Scaling and tuning modelsAbout the technologyDeep Learning with R, Third Edition is a practical, concept-driven introduction to modern deep learning for R users. With a focus on clarity, intuition, and hands-on experimentation, it guides you from the foundations of deep learning to advanced architectures such as transformers and LLMs. This book treats R as a fully capable environment for modern deep learning, showing how contemporary models and workflows can be developed end to end without compromise.About the bookDeep Learning with R, Third Edition gets you up to speed with the current state of deep learning practice. Using Keras 3 with R, you’ll build and train neural networks from scratch, work with transformers, fine-tune pretrained models and explore large language models and diffusion-based image generation. By following carefully constructed examples that build insight step-by-step, you’ll develop a deep understanding of why these models work—not just how to use them.What's inside• Hands-on, code-first learning in R• A clear progression from deep learning fundamentals to generative AI• Examples that emphasize intuition and understandingAbout the readerFor readers with intermediate R skills. No prior experience with deep learning is required.About the authorFrançois Chollet is the creator of Keras and author of Deep Learning with Python. Tomasz Kalinowski is a software engineer at Posit Software, PBC and maintainer of the Keras and TensorFlow R packages.Table of Contents1 What is deep learning?2 The mathematical building blocks of neural networks3 Introduction to TensorFlow, PyTorch, JAX, and Keras4 Classification and regression5 Fundamentals of machine learning6 The universal workflow of machine learning7 A deep dive into Keras8 Image classification9 Convnet architecture patterns10 Interpreting what convnets learn11 Image segmentation12 Object detection13 Timeseries forecasting14 Text classification15 Language models and the Transformer16 Text generation17 Image generation18 Best practices for the real world19 The future of AI20 Conclusions
| Publisher | Manning Publications |
| Publication date | June 2, 2026 |
| Edition | 3rd |
| Language | English |
| Print length | 648 pages |
| ISBN-10 | 1633435180 |
| ISBN-13 | 978-1633435186 |
| Item Weight | 1.65 pounds (750 grams) |
| Dimensions | 7.38 x 1.62 x 9.25 inches (18.7 x 4.1 x 23.5 cm) |
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Features & Benefits
- Learn deep learning fundamentals from scratch using R.
- Work through practical problems, including image classification and text processing.
- Gain confidence in building, tuning, and evaluating models.
- Use the Keras library to simplify the deep learning process.
- New chapters cover advanced topics like transformer architecture and image generation.
- No prior experience in advanced math is needed to start learning.
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