Deep Learning with R.

Format:

Sound recording

Author/Creator:

Chollet, François.

Contributor:

Kalinowski, Tomasz.

Language:

Undetermined

Subjects (All):

R (Computer program language).

Artificial intelligence.

Neural networks (Computer science).

Machine learning.

Mathematical statistics--Data processing.

Mathematical statistics.

Deep learning (Machine learning).

Computer vision.

Physical Description:

1 online resource (1 audio file)

Edition:

Third Edition.

Place of Publication:

Manning Publications 2026

Summary:

Deep learning from the ground up using R and the powerful Keras library! Deep Learning with R, Third Edition 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. 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 models 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. 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, 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 understanding About the Reader For readers with intermediate R skills. No prior experience with deep learning is required. About the Authors François 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. Quotes A clear, practical, and modern guide. - Hadley Wickham, Posit PBC A clear, practical, and modern guide. - Hadley Wickham, Posit PBC Indispensable resource for people interested in R and deep learning. - Kay Engelhardt, devstats.org A reference, not just for R users, but also for anyone trying to start their DL journey with a good feel and a fair amount of understanding. - Shahnawaz Ali, CRUK Scotland Institute Currently there is no better option for deep learning in R. The book is very complete and up to date. - Juan Delgado, Sodexo BRS.

Notes:

OCLC-licensed vendor bibliographic record.

OCLC:

1600467619