G
Gaël Varoquaux
Researcher at Université Paris-Saclay
Publications - 293
Citations - 108428
Gaël Varoquaux is an academic researcher from Université Paris-Saclay. The author has contributed to research in topics: Cognition & Computer science. The author has an hindex of 36, co-authored 264 publications receiving 87110 citations. Previous affiliations of Gaël Varoquaux include Centre national de la recherche scientifique & University of Florence.
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Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
The NumPy Array: A Structure for Efficient Numerical Computation
TL;DR: In this article, the authors show how to improve the performance of NumPy arrays through vectorizing calculations, avoiding copying data in memory, and minimizing operation counts, which is a technique similar to the one described in this paper.
Journal ArticleDOI
The NumPy array: a structure for efficient numerical computation
TL;DR: This effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts.
Journal ArticleDOI
Machine learning for neuroimaging with scikit-learn.
Alexandre Abraham,Alexandre Abraham,Fabian Pedregosa,Fabian Pedregosa,Michael Eickenberg,Michael Eickenberg,Philippe Gervais,Philippe Gervais,Andreas Mueller,Jean Kossaifi,Alexandre Gramfort,Alexandre Gramfort,Alexandre Gramfort,Bertrand Thirion,Bertrand Thirion,Gaël Varoquaux,Gaël Varoquaux +16 more
TL;DR: It is illustrated how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps and its application to neuroimaging data provides a versatile tool to study the brain.