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Book Review: Architects of Intelligence by Martin Ford

The new title “Architects of Intelligence – The Truth About AI from the People Building It,” by futurist Martin Ford is a real gem and worthy read for several groups of people: data scientists, AI researchers, enterprise decision makers, and while we’re at it, we can also throw in folks who are working to transition into this dynamic field that is in such high demand right now.

Book Review: The Model Thinker – A new way to look at Data Analysis

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.”

Book Review: How Smart Machines Think by Sean Gerrish

“How Smart Machines Think” by Sean Gerrish is a new MIT Press book that I would recommend to two classes of people: enterprise decision makers who are charged with evaluating AI, machine learning and deep learning technologies for their companies, and on the flip side, people who are looking to transition into the field of data science but know little about it. This is a great book for people in a hurry, something to pop into your carry-on bag the next time you’re on a cross country flight.

Book Review: Learning TensorFlow

Deep Neural Networks (DNNs), upon which deep learning is based, are trained with large amounts of data, and can solve complex tasks with unprecedented accuracy. TensorFlow is a leading open source software framework that helps you build and train neural networks. Here’s a nice resource to help you kick-start your use of TensorFlow – “Learning TensorFlow” by Tom Hope, Yehezkel S. Resheff and Itay Leider.

Book Review: Weapons of Math Destruction by Cathy O’Neil

Normally the books I review for insideBIGDATA play the role of cheerleader for our focus on technologies like big data, data science, machine learning, AI and deep learning. They typically promote the notion that utilizing enterprise data assets to their fullest extent will lead to the improvement of people’s lives. But after reading “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy,” by Cathy O’Neil, I can see that there’s another important perspective that should be considered.

Book Review: Python Data Science Handbook

I recently had a need for a Python language resource to supplement a series of courses on Deep Learning I was evaluating that depended on this widely used language. As a long-time data science practitioner, my language of choice has been R, so I relished the opportunity to dig into Python to see first hand how the other side of the data science world did machine learning. The book I settled on was “Python Data Science Handbook: Essential Tools for Working with Data” by Jake VanderPlas.

From the Editor’s Bookshelf: My Favorite Titles for Data Science and Machine Learning

As a practicing data scientist, I’ve spent years building up my library of academic and practical resources that I routinely draw upon for helping me do my work. Although my library is vast, I have a select group of books that occupy a prominent position on my desk. I’ve been asked enough times about my “favorite titles” list, I thought I’d write this article for my readers.

Book Review: The Future of IoT by Don DeLoach, Emil Berthelsen, and Wael Elrifai

“The Future of IoT – Leveraging the Shift to a Data Centric World,” by Don DeLoach, Emil Berthelsen, and Wael Elrifai is a self-published gem for anyone wondering about IoT. The overarching theme of the book is consistently – data, information, and knowledge – with a wrapper of use case examples to make it real. The book will assist you in kick-starting your evaluation of IoT technology in terms of all that data and how best to capitalize on it.

Book Review: Statistical Learning with Sparsity – The Lasso and Generalizations

As a data scientist, I have a handful of books that serve as important resources for my work in the field – “Statistical Learning with Sparsity – The Lasso and Generalizations” by Trevor Hastie, Robert Tibshirani, and Martin Wainwright is one of them. This book earned a prominent position on my desk for a number of reasons.

Book Review: The Mathematical Corporation by Josh Sullivan and Angela Zutavern

As a data scientist, I know first hand how today’s enterprise has some catching up to do with engaging the mathematical foundations for capitalizing on an ever-increasing volume of data assets. This is why a new title is so important: “The Mathematical Corporation – Where Machine Intelligence and Human Ingenuity Achieve the Impossible,” by Josh Sullivan and Angela Zutavern. Sullivan and Zutavern are, respectively, senior vice president and vice president of Booz Allen Hamilton.