February 18th 2014
September 4th 2013
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!
May 11th 2013
I’m please to announce a new version for scikits.optimization. The main focus of this iteration was to finish usual unconstrained optimization algorithms.
- 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
May 2nd 2013
December 18th 2012
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.
November 15th 2012
It has been a while, too long for sure, since my last update on this scikit. I’m pleased to announce that some algorithms are finally fixed as well as some tests.
- Fixed Polytope/Simplex/Nelder-Mead
- Fixed the Quadratic Hessian helper class
Additional tutorials will be available in the next weeks.
July 5th 2011
March 8th 2011
When faced with a new dataset, the issue is to find how it should be analyzed. A lot of books addresses the theoretical way of doing it, but this book gives practical clues to do it. Besides, it isn’t based on commercial tools like MATLAB, but on open source tools that can be freely downloaded on the Internet.
February 1st 2011
October 5th 2010
There are some effects that are simpler than other. Digital ones are generally easier than analog ones, and purely digital filter are also easier than digitally-transformed analog ones. Linear filters such as passband, cutband, … are easy to digitally design, chorus can be achieved through some spectral computations, delay and reverbation are computationnally expensive but easy to code.
It said that analog devices have a unique sound that digital devices cannot achieve. In fact, much is due to the simplications that occur when digitizing an analog device. One of the most blatant examples is the overdrive, which I took from Simulanalog.
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