Here’s a new title that is a “must have” for any data scientist who uses the R language. It’s a wonderful learning resource for tree-based techniques in statistical learning, one that’s become my go-to text when I find the need to do a deep dive into various ML topic areas for my work. The methods […]
Book Review: Mathematics for Machine Learning
August 4, 2021 by
“Mathematics for Machine Learning” by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press, is an excellent way to learn the math behind the models. This review shall highlight all the ways this book is special among the competition. Of all the books I’ve reviewed thus far, this is my favorite. Read on to learn why.
Book Review: The Model Thinker – A new way to look at Data Analysis
December 26, 2018 by
In this special guest feature, Carol Wells reviews the new book by Scott E. Page entitled The Model Thinker. “A hands-on reference for the working data scientist, ‘The Model Thinker’ challenges us to consider that the historical methods we have used for data analysis are no longer adequate given the complexity of today’s world. The book opens by making the case for a new way of using mathematical models to solve problems, offers a close look at a number of the models, then closes with a pair of demonstrations of the method.”
Using Advanced MPI: Modern Features of the Message-Passing Interface
January 4, 2015 by
“These authors are experts in MPI, but more importantly, they are experts at teaching MPI. If you want to master MPI, there no better guides than this book and its companion.”
