Just after the release of ATK SD1, I updated my audio toolkit. I added a few optimizations on overdrive computations, and also for the base filter array exchange.
It’s time for a new release of the toolkit. Much has been done in terms of basic filters, but also to simplify usage (static and shared libraries are compiled, no need to reset the pipeline before calling process…).
I’ve explained in earlier posts how to simulate a simple overdrive circuit. I’ve also explained how I implemented this in QtVST (and yes, I should have added labels on those images!), which was more or less the predecessor of Audio TK.
The main problem with simulating non linear circuits is that it costs a lot. Let’s see if I can improve the timings a little bit.
A long time ago, I started implementing audio filters with a Qt GUI. I also started other pet projects in the same area, but I didn’t have a proper audio support library in C++ for that. Also Qt plugins are no longer an option (for me), I still hope to implement new filters in the future.
It seems that Packt Publishing is on a publishing spree on Machine Learning in Python. After Building Machine Learning Systems In Python for which I was technical reviewer, Packt published Learning Scikit-Learn In Python last November.
I started using Boost.Asio years ago for my professional occupation. I remember difficult hours trying to understand its help and its tutorials. Would that have been different with the book?
I recently had the opportunity to be a technical reviewer for the new Building Machine Learning Systems in Python. As I took part in the book, I won’t write a review like what I did for other books.
First, I have to say that I was impressed by the quality of the content. Although I had some things that I thought were not excellent (I still need to check how my reviews changed the book), it’s the best book I’ve read from Packt so far. It has a good balance between code and comprehension, which is an equilibrium that is rarely achieved.
I don’t think it is possible to write a better book on Machine Learning in Python, unless the ecosystem evolves with new algorithms. Which it will, and it will mean a new edition of the book! Neat!
Open Source software seems for the young generation as sure as the sun rises. And even if I witnessed the emergence of Open Source, I more often than not forget that there was a time when Linux didn’t exist. This recent history brought us a lot, but we may only have handpicked some of this revolution’s fruit. Eric Raymond is one of the guys behind this revolution, and he took some time to think about the changes it brought.
I had the opportunity from Packt Publishing to review the second edition of Numpy Beginner’s Guide. Many thanks to the publisher for this and let’s go to the review.