Tag Archives: Python

Book: Building Machine Learning Systems in Python

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Almost 18 months ago, I posted a small post on the first version of this book (http://matt.eifelle.com/2013/09/04/book-building-machine-learning-systems-in-python/). At the time, I was really eager to see the second edition of it. And there it is!

I had once again the privilege of being a technical reviewer for this book, and I havce to say that the quality didn’t lower one bit, it went even higher. Of course, there is still room for a better book, when all Python module for Machine Learning are even better. I guess that will be for the third edition!

To get the book from the publisher: https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-systems-python-second-edition

On other matters, the blog was quiet for a long time, I’m hoping to get some time to post a few new posts soon, but it is quite hard as I’m currently studying for another master’s degree!

ATKCompressor and ATKExpander Implementation with Audio ToolKit

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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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Announcement: Audio TK 0.2.0

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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…).

Continue reading Announcement: Audio TK 0.2.0

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

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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.

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

Book: Building Machine Learning Systems in Python

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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!

Annoucement: scikits.optimization 0.3

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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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