What’s the common point between the questions of cryptography (US and Australia), vaccines (and link to disease), vitamin C (to cure cancer), spending thousands on power cables for your sound system? Some people use their non-knowledge to bully experts. And I think this book answers the question of why this happens.
On my quest for a good Flask book, I saw this book from Tarek Ziade. We are more or less of the same generation, both from France and he wrote a far better introductory book to Python in French than mine. He also founded the French Python community (AFPY), so I always had a huge respect for the guy. And the book was appetizing.
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.