|Deep Learning with R|
Deep Learning with R is meant for statisticians, analysts, engineers, and students with a reasonable amount of R experience but no significant knowledge of machine learning and deep learning. You’ll learn from more than 30 code examples that include detailed commentary and practical recommendations. You don’t need previous experience with machine learning or deep learning: this book covers from scratch all the necessary basics. You don’t need an advanced mathematics background, either—high school level mathematics should suffice in order to follow along.
An introduction to a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology.
|Deep Learning with Keras Cheatsheet|
A quick reference guide to the concepts and available functions in the R interface to Keras. Covers the various types of Keras layers, data preprocessing, training workflow, and pre-trained models.
In-depth examples of using TensorFlow with R, including detailed explanatory narrative as well as coverage of ancillary tasks like data preprocessing and visualization. A great resource for taking the next step after you’ve learned the basics.
Introductory examples of using TensorFlow with R. These examples cover the basics of training models with the keras and tensorflow packages.