August 12th 2014

Announcement: Audio TK 0.5.0

Audio Toolkit is now almost ready for its first stable release. Its content will now move toward more advanced DSP algorithms (zero delay filters, amplifiers).

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July 22nd 2014

ATKCompressor and ATKExpander Implementation with Audio ToolKit

I’d like to talk a little bit about the way a compressor and an expander can be written with the Audio Toolkit. Even if both effects use far less filters than the SD1 emulation, they still implement more than just one filter in the pipeline, contrary to a fixed delay line (another audio plugin that I released with the compressor and the expander).

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July 8th 2014

Announcement: Audio TK 0.4.0

A lot has happened in two weeks for Audio ToolKit. This release mainly adds tools for Compressor and Delays design. There will be additional plugins release soon.

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June 26th 2014

Announcement: Audio TK 0.3.0

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.

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June 10th 2014

Announcement: Audio TK 0.2.0

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…).

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February 18th 2014

Book review: Learning scikit-learn – Machine Learning in Python

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.

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September 4th 2013

Book: Building Machine Learning Systems in Python

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!

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May 11th 2013

Annoucement: scikits.optimization 0.3

I’m please to announce a new version for scikits.optimization. The main focus of this iteration was to finish usual unconstrained optimization algorithms.

Changelog

  • Fixes on the Simplex state implementation
  • Added several Quasi-Newton steps (BFGS, rank 1 update…)

The scikit can be installed with pip/easy_install or downloaded from PyPI

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May 2nd 2013

Comparison of optimization algorithms

In the next version of scikits.optimization, I’ve added some Quasi-Newton steps. Before this version is released, I thought I would compare several methods of optimizing the Rosenbrock function.
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December 18th 2012

Optimization scikit: Polytope (Simplex/Nelder-Mead) optimization

Now that version 0.2 of scikit.optimization is out, here is a tutorial on the gradient-free optimizer based on the simplex algorithm.

When the only thing you have is the cost function and when you don’t have dozens of parameters, the first thing that can be tried is a simplex algorithm.

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