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.
I have to say, I was intrigued when I saw the book. Lots of things about music seem intuitive, from movies to how it makes us feel. And the book puts a theoretical aspect on it. So definitely something I HAD to read.