The UNIX Programming Environment by Brian W. Kernighan My rating: 0 of 5 stars This book, copyright 1984, is not one’s typical software read. Typical books on software deal with the latest and greatest that’s coming down the pike. Instead, this book is a reminder of what is great in the UNIX operating system. It harkens back to the days when assembly coding was common and programming in C was considered more cutting edge. So…
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes by Arjun Panesar My rating: 4 of 5 stars After the first chapter of this book, I was ready to put it down and regret the money I spent on it. It seemed to walk over ground that I’ve already covered as a researcher in medical informatics. Fortunately, I continued, for I came to learn a lot from this author. Although not as…
Design Patterns: Elements of Reusable Object-Oriented Software by Erich Gamma My rating: 0 of 5 stars A classic in the field of computer science, this book describes how object-oriented software can be used to develop meaningful (and reusable) classes. It is written by the so-called “gang of four” affiliated with a working group at the University of Illinois. It has achieved notoriety for its assistance in helping programmers write code more effectively in languages like…
Mastering Deep Learning: A Complete Introduction for Beginners and Newbies by James Gabriel My rating: 0 of 5 stars Deep Learning seeks to mimic how humans learn (i.e., the brain processes of how humans learn in their cerebral cortex) and apply this mimicry to how computer programs are written. Thus, we have terms like a “neural network” which does not refer to a brain (made up of neurons) but to a web of computer cells…
by Michael WalkerCopyright (c) 2018. This book sits in a series by the publishing house AI Sciences that traverses topics in the field of Artificial Intelligence to make these subjects more accessible for the masses. I bought this book’s Kindle Edition for only $5. Interestingly, this was one of the most expensive items in the series. I am glad to have taken this short (77-page) book for a perusal. It reviewed some of my prior…
Introduction to Natural Language Processing: Concepts and Fundamentals for Beginners by Michael Walker Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes by Arjun Panesar
by Camille FournierCopyright (c) 2017 My path to software was not traditional. I always did well at mathematics in school, but I liked many things that weren’t technical – journalism, religion, poetry, and medicine all pulled my strings at some time. I have ended up producing software used in medical research. As such, I figured that I needed to study the traditional career path in software/technology to try to meld my diverse skill-set with more…
by Frederick P. BrooksCopyright (c) 1995 What is relevant about a book, in its second edition, that was originally written a generation or two ago about managing computer projects? The author Brooks led the management of the project for IBM decades ago. The answer to this question is simple and is evident in the title. Scaling software projects from smaller-to-larger does not scale linearly. In case you don’t know what this means, scaling non-linearly means…
by Martin FolwerCopyright (c) 1999 I picked up this book at the wrong time. The book was so successful that a second edition is due out on November 30, 2018 (less than two weeks from now). On the other hand, I picked up this book at the right time. At work, my project is in the midst of a refactoring project. I am in the middle of changing PHP code from modular functions to object-orientation.…