I’ve been a big fan of MIT mathematics professor Dr. Gilbert Strang for many years. A few years ago I reviewed the latest 5th edition of his venerable text on linear algebra. Then last year I learned how he morphed his delightful mathematics book into a brand new title (2019) designed for data scientists – “Linear Algebra and Learning from Data.” I was intrigued, so after I received my review copy I did a deep dive without hesitation.
Book Review: Python Machine Learning – Third Edition by Sebastian Raschka, Vahid Mirjalili
February 19, 2020 by Leave a Comment
I had been looking for a good book to recommend to my “Introduction to Data Science” classes at UCLA as a text to use once my class completes … sort of the next step after learning the basics. That’s why I was looking forward to reviewing the new 3rd edition of the widely acclaimed title “Python Machine Learning” by Sebastian Raschka, Vahid Mirjalili. The book is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a useful resource you’ll keep coming back to as you fill up your data science toolbox.
Front Cover of a Book Entirely Created by Artificial Intelligence
January 31, 2020 by 2 Comments
The newly released book “The A.I. Age” represents a historic moment and new
landmark in the story of AI with its February 2020 publication. The front cover of the book has been entirely created by Artificial Intelligence (AI) by selecting the winning cover design and overlaying the text.
Book Excerpt: Defining Data Models
October 14, 2019 by Leave a Comment
The following article is an excerpt (Chapter 3) from the book Hands-On Big Data Modeling by James Lee, Tao Wei, and Suresh Kumar Mukhiya published by our friends over at Packt. The article addresses the concept of big data, its sources, and its types. In addition to this, the article focuses on giving a theoretical foundation about data modeling and data management. Readers will be getting their hands dirty with setting up a platform where we can utilize big data.
Book Review: AI Blueprints by Dr. Joshua Eckroth
July 3, 2019 by Leave a Comment
Having just finished teaching a couple of introductory data science classes this past academic quarter, I came to the realization that it’s hard for newbie data scientists to get started on a project of reasonable complexity. Many students got frustrated in establishing a framework (or “blueprint”) with which to start building their machine learning applications for their class project. A new title from Packt Publishing, “AI Blueprints,” by Dr. Joshua Eckroth, helps solve this problem by laying out six real-life business scenarios for how AI can solve critical challenges with state-of-the-art AI software libraries and a well thought out workflow.
Book Review: Deep Learning Revolution by Terrence J. Sejnowski
March 5, 2019 by Leave a Comment
The new MIT Press title “Deep Learning Revolution,” by Professor Terrence J. Sejnowski, offers a useful historical perspective coupled with a contemporary look at the technologies behind the fast moving field of deep learning. This is not a technical book about deep learning principles or practices in the same class as my favorite “Deep Learning” […]
Book Review: Architects of Intelligence by Martin Ford
January 15, 2019 by Leave a Comment
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
January 3, 2019 by Leave a Comment
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
November 23, 2018 by Leave a Comment
“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
January 9, 2018 by Leave a Comment
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.
