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AI Paris 2019 in one picture

Posted on Mon 17 June 2019 in Meeting • Tagged with Python • 3 min read

This week, I was at the AI Paris 2019 event to represent Kernix. We had great talks with so many people, and I barely had time to go around to look what other companies were working on. That is why I look at this afterwards. Can we have a big picture of the event without being there?

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Accuracy: from classification to clustering evaluation

Posted on Tue 04 June 2019 in machine learning • Tagged with evaluation measure, clustering, Python • 4 min read

Accuracy is often used to measure the quality of a classification. It is also used for clustering. However, the scikit-learn accuracy_score function only provides a lower bound of accuracy for clustering. This blog post explains how accuracy should be computed for clustering.

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Dense matrices implementation in Python

Posted on Mon 04 February 2019 in coding • Tagged with Python • 8 min read

Machine learning algorithms often use matrices to store data and compute operations such as multiplications or singular value decomposition. The purpose of this article is to see how matrices are implemented in Python: how the data is stored and how much memory it consumes.

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Data classes in Python

Posted on Sat 27 October 2018 in coding • Tagged with Python • 5 min read

This blob post presents the brand-new Python data classes feature and its benefits.

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Static typing in Python

Posted on Sat 13 October 2018 in coding • Tagged with Python • 5 min read

This article covers static typing in Python: how and why type annotate Python code, how to type check statically, and how to enable powerful IDEs features.

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Encoding in Python

Posted on Sat 29 September 2018 in coding • Tagged with Python • 6 min read

The transition from Python 2 to Python 3 caused some problems since the two versions handle text differently. First, we will see how the text is represented in Python 2 and Python 3, then how to do the conversion between the different representations, and then the different places where encoding step in: the encoding of the source code, the implicit conversions, the encoding of the inputs and outputs, and the file system encoding.

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Key differences between mainly used languages for data science

Posted on Sat 01 September 2018 in Coding • Tagged with Javascript, Python, Scala, C • 6 min read

This blog post introduces the notions of strongly and weakly typed in one hand, and the notion of static and dynamic typing in the other hand. It is illustrated with four languages commonly used in data science pipelines.

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