All major cloud providers provide some support for Machine Learning algorithms. They also evolve all the time. There are not many books ont he subject, due to the evolution of these services, so let’s have a look at this one.
A few weeks ago, I presented my work on automatic code generation from an electronic schema. I have many things to talk about this subject, one of them is this book.
When you start analyzing a circuit, it is important to learn how to analyze a circuit. There are lots of books on electronics, but this one targets beginners in circuit analysis.
A decade ago, the objective was to have a build farm and do continuous integration (on each commit, build the application and run unit tests). Now, the objective is continuous delivery. This means that the new build is directly put into production. All the major applications are doing this, from Chrome to Spotify. You may not get every version on your machine, but you should consider a build as something you could deploy.
The nice thing is that there are tools to ease this workflow.
A few year ago, Packt Publishing contacted to be a technical reviewer for the first edition of Building Machine Learning Systems with Python, and I was impressed by the writing of Luis Pedro Coelho and Willi Richert. For the second edition, I was again a technical reviewer.
Writing is not easy, especially when it’s not your mother tongue, and scientific books are plagued with books that are not that great, with low technical content or bad English (that can be said for novels as well, the worst I ever read probably being the Hunger games series…). Even if I don’t like the books, I know that the authors did their best, having written in the past a book that I can say was not very great in terms of flow. Writing a book always deserves the deepest respect.
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.
After the book on LLVM core libraries, I want to have a look at the cookbook.
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!