I’m thinking of writing a Web service for a project of mine. For this purpose, I wanted to learn Flask (and a bunch of other technologies), as Flask seems well established and well documented. This is a book from Packt that agglomerates 3 previously released books. One of the main questions is the relevance of them as the Flask API evolves.
ATK is updated to 3.1.0 with heavy code refactoring. Old C++ standards are now dropped and it requires now a full C++17 compliant compiler.
The main difference for filter support is that explicit SIMD filters using libsimdpp have been dropped while tr2::simd becomes standard and supported by gcc, clang and Visual Studio.
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.
Last month, I presented my latest work on Audio ToolKit at ADC 2018, namely how I turned a SPICE netlist to a filter.
It is now time to present some of the results here.
I started my Lego adult path with the Mk2 crane, and now Lego has a new crane. This one is bigger, meaner, in some aspects, but hopefully better as well. Bigger wheels, but half of them, red instead of yellow, broader, and double crane boon instead of a triple one, so a different set of compromises. How did it go?
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 few weeks ago, on StackOverflow, a user asked for an accuracy measure on the embedded space for an autoencoder. This was with Keras, but I thought it would be a nice exercise for Tensorflow as well.
The idea in this case is to add a few layers to the embedded space to create a classifier and measure its accuracy while we optimize the autoencoder.
We will train the autoencoder in alternation with the classifier. When one is updated, the other will be frozen, and then we can measure classification accuracy and reconstruction loss concurrently in Tensorboard.
Today, I’m presenting at the ADC my work on analog modelling for the past year.
I will make a more detailed post later this year, but I’d like to put some teasers here. SPICE net lists are an efficient way of representing electronics circuits and there are several very good free and paying simulators. Unfortunately, they are not easy to integrate in a VST plugin.
Audio ToolKit now has a sister project around this topic. The lite version is also licensed under the BSD and can generate a dynamic filter of a net list. The full project is now also capable of generating static filter, with a source file (and compiling it in memory) that can be manually tuned.
Future work on this project will include different solvers for the static filter, as well as a tuner that will be able to drop entries in the Jacobian (full entries or component contributions for a given pin) in the Newton Raphson solver.
This year, Lego published a set based on the Bugatti Chiron, one of the craziest cars, and built near my home town. It’s the second set in the Technic car collection series, and contrary to the Porsche, the color is inspired by a gorgeous real life car (I don’t think that the real Porsche exists…).
Audio ToolKit started with only C++11 a long time ago, and now with version 3.1, it’s going to be full C++17.