: Exploration of Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks.
(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...
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. : Absolute beginners in programming or mathematics, as
: Absolute beginners in programming or mathematics, as the book lacks practice problems with answers and assumes a high level of prerequisite knowledge. Summary Table Reality Check Prerequisites Strong background in R and Advanced Math Code-to-Theory Ratio Theory-heavy (~80% math) Topics Covered CNNs, RNNs, Autoencoders, Optimization Primary Critique Mathematical inaccuracies and typos in early chapters : Exploration of Autoencoders