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Tech Book Face Off: Programming Massively Parallel Processors Vs. Professional CUDA C Programming

After getting an introduction to GPU programming with CUDA by Example, I wanted to dig in deeper and get to know the real ins and outs of CUDA programming. That desire quickly lead to the selection of books for this Tech Book Face Off. The first book is definitely geared to be a college textbook, and as I spent years learning from books like this, I felt comfortable taking a look at Programming Massively Parallel Processors: A Hands-on Approach by David B. Kirk and Wen-mei W. Hwu. The second book is targeted more at the working professional, as the title suggests: Professional CUDA C Programming by John Cheng, Max Grossman, and Ty McKercher. I was surprised by both books, and not in the same way. Let's see how they do at teaching CUDA programming.

Programming Massively Parallel Multiprocessors front coverVS.Professional CUDA C Programming front cover

Tech Book Face Off: Data Smart Vs. Python Machine Learning

After reading a few books on data science and a little bit about machine learning, I felt it was time to round out my studies in these subjects with a couple more books. I was hoping to get some more exposure to implementing different machine learning algorithms as well as diving deeper into how to effectively use the different Python tools for machine learning, and these two books seemed to fit the bill. The first book with the upside-down face, Data Smart: Using Data Science to Transform Data Into Insight by John W. Foreman, looked like it would fulfill the former goal and do it all in Excel, oddly enough. The second book with the right side-up face, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow by Sebastian Raschka and Vahid Mirjalili, promised to address the second goal. Let's see how these two books complement each other and move the reader toward a better understanding of machine learning.

Data Smart front coverVS.Python Machine Learning front cover

Tech Book Face Off: Seven Concurrency Models in Seven Weeks Vs. CUDA by Example

Concurrency and parallelism are becoming more important by the day, as processor cores are becoming more numerous per CPU and more widespread in every type of computing device, while single core performance is stagnating. Something that used to be barely accessible to the average programmer is now becoming ubiquitous, which makes it even more pertinent to learn how to utilize all of these supercomputers effectively. Besides, parallel processing is a fascinating topic, and I think it's great that it is now so easy to experiment at home with things that used to be reserved for huge companies and university research departments. In order to become more proficient at programming in this way, I started with the book Seven Concurrency Models in Seven Weeks: When Threads Unravel by Paul Butcher for an overview of the current state of affairs in concurrent and parallel programming. Then I went for an introduction to CUDA programming for GPUs with CUDA by Example by Jason Sanders and Edward Kandrot. I've been looking forward to digging into these fascinating books for a while now, so let's see how they stack up.

Seven Concurrency Models in Seven Weeks front coverVS.CUDA By Example front cover

Tech Book Face Off: Effective Python Vs. Data Science from Scratch

I must confess, I've used Python for quite some time without really learning most of the language. It's my go-to language for modeling embedded systems problems and doing data analysis, but I've picked up the language mostly through googling what I need and reading the abbreviated introductions of Python data science books. It was time to remedy that situation with the first book in this face-off: Effective Python: 59 Specific Ways to Write Better Python by Brett Slatkin. I didn't want a straight learn-a-programming-language book for this exercise because I already knew the basics and just wanted more depth. For the second book, I wanted to explore how machine learning libraries are actually implemented, so I picked up Data Science from Scratch: First Principles with Python by Joel Grus. These books don't seem directly related other than that they both use Python, but they are both books that look into how to use Python to write programs in an idiomatic way. Effective Python focuses more on the idiomatic part, and Data Science from Scratch focuses more on the writing programs part.

Effective Python front coverVS.Data Science from Scratch front cover

Physics Book Face Off: The Hidden Reality Vs. Parallel Worlds

It has been an awfully long time since my last Physics Book Face Off, and that's mostly because it seems that I read pop physics books very slowly. I haven't even gotten into the real physics books that I eventually plan to read on relativity, string theory, and the like, but that's okay because I'm still enjoying these gentle forays into the technical details of the universe. For this pair of physics books, I dug into the idea of multiple universes with The Hidden Reality: Parallel Universes and the Deep Laws of the Cosmos by Brian Greene and Parallel Worlds: A Journey Through Creation, Higher Dimensions, and the Future of the Cosmos by Michio Kaku. Physicists have a bunch of different ideas about the possibility of other universes lying outside our own, and both of these books, by authors I've read before, set out to explore those wide-ranging ideas.

The Hidden Reality front coverVS.Parallel Worlds front cover

Tech Book Face Off: Rails AntiPatterns Vs. The Rails 5 Way

It's been a while since I've cracked open a Ruby on Rails book, and there were still a couple of these books that I've been meaning to read. So far the Rails books I've read have been beginner's books and tutorials. With this latest pair of books, I wanted to go deeper into Rails and learn how to program fluently in the framework. The first book, Rails AntiPatterns: Best Practice Ruby on Rails Refactoring by Chad Pytel and Tammer Saleh, was published way back in 2010 when Rails 2.3 was cutting edge. I took a chance that the book would focus more on timeless advice than version-specific tips and tricks. The opposing book, The Rails 5 Way by Obie Fernandez, was published much more recently at the end of 2017 and should be at less risk of being out-of-date. Although, it looks like Rails 6 will be out soon. One thing's for sure: technology doesn't stand still, but that shouldn't matter too much if the books take that into account. Let's see how they fare.

Rails AntiPatterns front coverVS.The Rails 5 Way front cover

Tech Book Face Off: Python for Data Analysis Vs. Python Data Science Handbook

I'm starting to dabble in machine learning. (You know it's all the rage now.) As with anything new, I find it most effective to pick out a couple of books on the subject and start learning the landscape and the details straight away. Online resources are good for an introduction, or to find answers to specific questions on how to get a particular task done, but they don't hold a candle to the depth and focus that you can find from reading about a subject in a well-written book. Since I'd already had some general exposure to machine learning in college, I wanted to work through a couple of books that focused on how to do data analysis and machine learning in a practical sense with a real language and modern tools. Python with Pandas and Scikit-Learn has a huge community and plenty of active development right now, so that's the route I went with for this pair of books. I selected Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney to get the details of using the Pandas data analysis package from the author of the package himself. Then I chose Python Data Science Handbook: Essential Tools for Working with Data by Jake VanderPlas to get more coverage of Pandas from another perspective and expand into some of the Scikit-Learn tools available for machine learning. Let's see how these two books stack up for learning to make sense of large amounts of data.

Python for Data Analysis front coverVS.Python Data Science Handbook front cover