Deep Learning with R, Third Edition by François Chollet (.ePUB)
File Size: 56.5 MB
Deep Learning with R, Third Edition: From first principles to generative AI by François Chollet, Tomasz Kalinowski
Requirements: .ePUB reader, 56.5 MB
Overview: 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. 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. Deep 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. For readers with intermediate R skills. No prior experience with Deep Learning is required.
Genre: Non-Fiction > Tech & Devices
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