Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you'll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library.

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.