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.

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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.

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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.

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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.

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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.

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