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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.

Book Excerpt: Minding the Machines

The following article is an excerpt from the new book Minding the Machines: Building and Leading Data Science and Analytics Teams by AI and analytics strategy expert Jeremy Adamson, published by John Wiley & Sons, Inc. Organize, plan, and build an exceptional data analytics team within your organization

Book Excerpt: Real World AI

This article was adapted from the recently released best-selling book, Real World AI, written by Alyssa Rochwerger and Wilson Pang. Alyssa is the director of product at Blue Shield of California and has previously served as VP of product for Figure Eight (acquired by Appen), VP of AI and data at Appen, and director of product at IBM Watson. Wilson is the CTO of Appen and has over nineteen years’ experience in software engineering and data science, having served as the chief data officer of Ctrip and the senior director of engineering at eBay.

Book Review: Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning

We’re seeing a rising number of new books on the mathematics of data science, machine learning, AI and deep learning, which I view as a very positive trend because of the importance for data scientists to understand the theoretical foundations for these technologies. In the coming months, I plan to review a number of these […]

Book Review: Deep Reinforcement Learning Hands-On

RL is a hugely popular area of deep learning, and many data scientists are exploring this AI technology to broaden their skillet to include a number of important problem domains like chatbots, robotics, discrete optimization, web automation and much more. As a result of this wide-spread interest in RL, there are many available educational resources specifically tailored to this class of deep learning – boot camps, training certificates, educational specializations, etc. But if you’re a data scientist who has been programming in Python for a while, and has some experience with other forms of deep learning using a framework like TensorFlow, then maybe this new book, “Deep Reinforcement Learning Hands-On,” by Maxim Lapan, might be a great way to kick-start yourself into becoming productive with RL.