{"product_id":"9781633435186","title":"Deep Learning with R, Third Edition: From first principles to generative AI","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eChollet, François; Kalinowski, Tomasz\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e6\/2\/2026\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eGet the eBook free when you register your print book at Manning.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eThis 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.\u003cbr\u003e \u003cbr\u003e 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.\u003cbr\u003e \u003cbr\u003eIn \u003ci\u003eDeep Learning with R, Third Edition\u003c\/i\u003e you will learn:\u003cbr\u003e \u003cbr\u003e • Deep learning from first principles\u003cbr\u003e • The latest features of Keras\u003cbr\u003e • Image classification and image segmentation\u003cbr\u003e • Time series forecasting\u003cbr\u003e • Text classification and machine translation\u003cbr\u003e • Text and image generation—build your own LLMs and diffusion models!\u003cbr\u003e • Scaling and tuning models\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003ci\u003eDeep Learning with R, Third Edition\u003c\/i\u003e 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.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003ci\u003eDeep Learning with R, Third Edition\u003c\/i\u003e 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.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e • Hands-on, code-first learning in R\u003cbr\u003e • A clear progression from deep learning fundamentals to generative AI\u003cbr\u003e • Examples that emphasize intuition and understanding\u003cbr\u003e \u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e For readers with intermediate R skills. No prior experience with deep learning is required.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e \u003cb\u003eFrançois Chollet\u003c\/b\u003e is the creator of Keras and author of \u003ci\u003eDeep Learning with Python\u003c\/i\u003e. \u003cb\u003eTomasz Kalinowski\u003c\/b\u003e is a software engineer at Posit Software, PBC and maintainer of the Keras and TensorFlow R packages.\u003cbr\u003e \u003cbr\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e 1 What is deep learning?\u003cbr\u003e 2 The mathematical building blocks of neural networks\u003cbr\u003e 3 Introduction to TensorFlow, PyTorch, JAX, and Keras\u003cbr\u003e 4 Classification and regression\u003cbr\u003e 5 Fundamentals of machine learning\u003cbr\u003e 6 The universal workflow of machine learning\u003cbr\u003e 7 A deep dive into Keras\u003cbr\u003e 8 Image classification\u003cbr\u003e 9 Convnet architecture patterns\u003cbr\u003e 10 Interpreting what convnets learn\u003cbr\u003e 11 Image segmentation\u003cbr\u003e 12 Object detection\u003cbr\u003e 13 Timeseries forecasting\u003cbr\u003e 14 Text classification\u003cbr\u003e 15 Language models and the Transformer\u003cbr\u003e 16 Text generation\u003cbr\u003e 17 Image generation\u003cbr\u003e 18 Best practices for the real world\u003cbr\u003e 19 The future of AI\u003cbr\u003e 20 Conclusions","brand":"Manning","offers":[{"title":"Default Title","offer_id":47086814331135,"sku":"9781633435186","price":79.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/9781633435186_s600x595.jpg?v=1780500799","url":"https:\/\/massivebookshop.com\/de\/products\/9781633435186","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}