Tomasz Kalinowski – författare

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. Youll appreciate the intuitive explanations, crisp illustrations, and clear examples. In this expanded third edition youll 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 generationbuild your own LLMs and diffusion models! Scaling and tuning models About the technology Deep 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 book Deep Learning with R, Third Edition gets you up to speed with the current state of deep learning practice. Using Keras 3 with R, youll 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, youll develop a deep understanding of why these models worknot 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 understanding About the reader For readers with intermediate R skills. No prior experience with deep learning is required. About the author Franois 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 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 into 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 20 Conclusions

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. 

Deep learning from the ground up using R and the powerful Keras library! In   Deep Learning with R, Second Edition  you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition  shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling   Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. about the technology

Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. what's inside

Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation about the reader

For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.