I work on a day-to-day basis on a big project that has many developers with different C++ level. Scott Meyers wrote a wonderful book on modern C++ (that I still need to review one day, especially since there is a new Effective Modern C++), but it is not for beginners. So I’m looking for that rare book with modern C++ and an explanation of good practices.
LLVM has always intrigued me. Actually, I always thought about one day writing a compiler. But it was more a challenge than a requirement for any of my works, private or professional, so never dived into it. The design of LLVM was also very well thought, and probably close to something I would have had liked to create.
So now the easiest is just to use LLVM for the different goals I want to achieve. I recently had to write clang-tidy rules, and I also want to perhaps create a JIT for Audio Toolkit and the modeling libraries. So lots of reasons to look at LLVM.
Recently, I moved to the finance industry. As usually when I start in a new domain, I look at the Python books for it. And Python for Finance from Yves Hilpisch is one of the most known ones.
I like change. More precisely, I like improving things. An as some of the people in my entourage would say, I can be a bull in a china shop. So this book sounded interesting.
I love reading books on signal processing, especially on audio signal processing. From books on using effects to a so-called reference, I still enjoy reading them, even if they are frustrating. The only one that was is DAFX: Digital Audio Effects, but I haven’t made a review of it!
American universities have some reputation, in all kind of terms, and the amount of student debt is something I also found baffling. So a book on the failure of US universities was obviously of interest to me.
This review will actually be quite quick: I haven’t finished the book and I won’t finish it.
The book was published in August 2015 and is based on OpenGL < 3. The authors may sometimes say that you can use shaders to do better, but the fact is that if you want to execute the code they propose, you need to use the backward compatibility layer, if it's available. OpenGL was published almost a decade ago, I can't understand why in 2015 two guys decided that a new book on scientific visualization should use an API that was deprecated a long time ago. What a waste of time.
Big data is the current hype, the thing you need to do to find the best job in the world. I’ve started using machine learning tools a decade ago, and when I saw this book, it felt like it was answering some concerns I had. Let’s see what’s inside.