February 18th 2014
January 7th 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!
August 20th 2013
Open Source software seems for the young generation as sure as the sun rises. And even if I witnessed the emergence of Open Source, I more often than not forget that there was a time when Linux didn’t exist. This recent history brought us a lot, but we may only have handpicked some of this revolution’s fruit. Eric Raymond is one of the guys behind this revolution, and he took some time to think about the changes it brought.
June 13th 2013
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
January 22nd 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.