What Does the Commercialization of Generative AI Mean for Society?

ACM, the Association for Computing Machinery has released “TechBrief: Generative Artificial Intelligence.” It is the latest in the quarterly ACM TechBriefs series of short technical bulletins that present scientifically grounded perspectives on the impact and policy implications of specific technological developments in computing.

Book Review: The Kaggle Book/Workbook

Kaggle is an incredible resource for all data scientists. I advise my Intro to Data Science students at UCLA to take advantage of Kaggle by first completing the venerable Titanic Getting Started Prediction Challenge, and then moving on to active challenges. Kaggle is a great way to gain valuable experience with data science and machine learning. Now, there are two excellent books to lead you through the Kaggle process. The Kaggle Book by Konrad Banachewicz and Luca Massaron published in 2022, and The Kaggle Workbook by the same authors published in 2023, both from UK-based Packt Publishing, are excellent learning resources.

Book Review: Math for Deep Learning

One of my favorite learning resources for gaining an understanding for the mathematics behind deep learning is “Math for Deep Learning” by Ronald T. Kneusel from No Starch Press. If you’re interested in getting quickly up to speed with how deep learning algorithms work at a basic level, then this is the book for you.

Book Review: Tree-based Methods for Statistical Learning in R

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: Machine Learning with PyTorch and Scikit-Learn

The enticing new title courtesy of Packt Publishing, “Machine Learning with PyTorch and Scikit-Learn,” by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili is a welcome addition to any data scientist’s list of learning resources. This 2022 tome consists of 741 well-crafted pages designed to provide a comprehensive framework for working in the realm of machine learning and deep learning. The book is brimming with topics that will propel you to a leading-edge understanding of the field.

Book Review: Modern Data Science with R, 2nd Edition

There’s good reason why the word “modern” is in the title of this new title from CRC Press: “Modern Data Science with R, 2nd,” by 3 professors Benjamin S. Baumer, Daniel T. Kaplan, and Nicholas J. Horton – the goal of the text is to provide a solid guide for state-of-the-art data science with the […]

Book Review: Advanced R Solutions

Hadley Wickham’s popular “Advanced R” book has many intriguing exercises that test your knowledge of deep operations of the R environment. The subject of this book review is a brand new title, “Advanced R Solutions” which is a well-crafted answer book containing all the solutions to each exercise appearing in Advanced R. So many parts of the R language are highlighted in the exercises and solutions, including important topics like: names and values, vectors, subsetting, flow of control, functions, plus large topics like functional programming, object-oriented programming, metaprogramming. Another couple of chapters deal with measuring and improving performance.

Book Review: Synthetic Data for Deep Learning

“Synthetic Data for Deep Learning,” by Sergey I. Nikolenko (published by Springer), represents a very good academic treatment of the subject. But what gives the book more street cred is the fact that the author is also Chief Research Officer for Synthesis AI, a start-up company pioneering this accelerating field. It’s nice to know the book represents both the academic and practical perspectives of the topic.

Cloudera Shines Educational Spotlight on Data and AI with Children’s Book for 8- to 12-year-olds

Cloudera, Inc., the enterprise data cloud company, announced “A Fresh Squeeze on Data,” a downloadable children’s book that explains simple ways to problem solve with data in a manner that kids can understand. The book was created in partnership with education company ReadyAI, with the goal of making data and AI more interesting and accessible to 8- to 12-year-olds.

Book Review: Mathematics for Machine Learning

“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.