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: Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
September 28, 2020 by Leave a Comment
Book Review: Deep Reinforcement Learning Hands-On
August 25, 2020 by Leave a Comment
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.
Book Review: Deep Learning with TensorFlow 2 and Keras
August 4, 2020 by 4 Comments
If you’re a data scientist who has been wanting to break into the deep learning realm, here is a great learning resource that can guide you through this journey. It’s pretty much an all-inclusive resource that includes all the popular methodologies upon which deep learning depends: CNNs, RNNs, RL, GANs, and much more. The glue that makes it all work is represented by the two most popular frameworks for deep learning pratcitioners, TensorFlow and Keras.
Book Review: Bayesian Statistics the Fun Way by Will Kurt
July 21, 2020 by Leave a Comment
“Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, Lego, and Rubber Ducks,” by Will Kurt (2019 No Starch Press) is an excellent introduction to subjects critical to all data scientists. Will Kurt, in fact, is a data scientist! I always advise my data science classes at UCLA to engage these important subjects in order to obtain a well-rounded exposure to disciplines upon which data science is based. I’ve already added this title to my official bibliography of learning resources given to my students.
Book Review: The Art of Statistics – How to Learn from Data by David Spiegelhalter
July 9, 2020 by Leave a Comment
This recent title, “The Art of Statistics – How to Learn from Data,” by University of Cambridge statistician David Spiegalhalter, is an important book on a number of fronts. I particularly appreciated the topics covered in the book that touch on important parts of the Data Science Process: data visualization, linear regression, logarithmic scales, Pierson correlation coefficient, data distributions, logistic regression, ROC curves, classification trees, over-fitting, bootstrap, probability theory, probability distributions, Bayes theory, and much more. I think new data scientists should engage a gentle introduction of these topics before diving into mathematical theory and code.
Book Review: Linear Algebra and Learning from Data by Gilbert Strang
June 11, 2020 by 6 Comments
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 1 Comment
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.
