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.
Cloudera Shines Educational Spotlight on Data and AI with Children’s Book for 8- to 12-year-olds
September 30, 2021 by Leave a Comment
Book Review: Mathematics for Machine Learning
August 4, 2021 by Leave a Comment
“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
July 10, 2021 by Leave a Comment
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
March 29, 2021 by Leave a Comment
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
September 28, 2020 by Leave a Comment
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
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.
