: Coverage of linear algebra, probability theory, and numerical computation.
(by Taweh Beysolow II) is a concise technical guide designed for those who want to bridge the gap between traditional data science and modern neural networks using the R language. Expert & Critical Perspective Introduction to Deep Learning Using R: A Step-b...
: Exploration of Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks. : Coverage of linear algebra, probability theory, and
The book is structured to take you from basic concepts to advanced architectures: The book is structured to take you from
: Tutorials on Single/Multilayer Perceptrons , Convolutional Neural Networks (CNNs) , and Recurrent Neural Networks (RNNs) .
: Digital versions have been criticized for poor formatting, making complex formulas small and difficult to read. Key Features & Content
If you are looking for more hands-on alternatives, you might consider the Deep Learning with R book by , which is often cited as a more practical, code-centric alternative.
