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.
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.
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.
After my previous post on SPICE modelling in Python, I need to use a good support example to go up to on the fly compilation in C++. This schema will also require some changes to support more than simple nodal analysis, so this now becomes Modified Nodal Analysis with state equations.
A few month ago, mystran published on KVR a small SPICE simulator for real-time processing. I liked the idea, the drawback being that the code is generic and not tailored like a static version of the optimizer. So I wondered if it was doable. But for this, I have to start from the basics and build from there. So let’s go.
ATK is updated to 2.3.0 with major fixes and code coverage improvement (see here). Lots of bugs were fixed during that effort and native build on embedded platforms was also fixed.
CMake builds on Linux don’t have to be installed before Python tests have to be ran. SIMD filters are now also easier to implement.