Deep learning enables efficient and accurate learning from massive amounts of data. Deep learning is being adopted by numerous industries at an increasing pace since it can help solve a number of challenges that cannot easily be solved by means of traditional machine learning techniques.
Developers working with R will be able to put their knowledge to work with this practical guide to deep learning. This book provides a hands-on approach to implementation and associated methodologies that will have you up and running and productive in no time. Complete with step-by-step explanations of essential concepts and practical examples, you will begin by exploring deep learning in general, including an overview of deep learning advantages and architecture. You will explore the architecture of various deep learning algorithms and understand their applicable fields. You will also learn how to build deep learning models, optimize hyperparameters, and evaluate model performance.
By the end of this book, you will be able to build and deploy your own deep learning models and applications using deep learning frameworks and algorithms specific to your problem.
