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Scikit-learn: Machine Learning in Python
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. This package focuses on bringingExpand
SciPy 1.0: fundamental algorithms for scientific computing in Python
TLDR
An overview of the capabilities and development practices of SciPy 1.0 is provided and some recent technical developments are highlighted. Expand
API design for machine learning software: experiences from the scikit-learn project
TLDR
The simple and elegant interface shared by all learning and processing units in the Scikit-learn library is described and its advantages in terms of composition and reusability are discussed. Expand
Machine learning for neuroimaging with scikit-learn
TLDR
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. Expand
SymPy: symbolic computing in Python
TLDR
The architecture of SymPy is presented, a description of its features, and a discussion of select domain specific submodules are discussed, to become the standard symbolic library for the scientific Python ecosystem. Expand
Hyperparameter optimization with approximate gradient
TLDR
This work proposes an algorithm for the optimization of continuous hyperparameters using inexact gradient information and gives sufficient conditions for the global convergence of this method, based on regularity conditions of the involved functions and summability of errors. Expand
ASAGA: Asynchronous Parallel SAGA
TLDR
It is proved that ASAGA can obtain a theoretical linear speedup on multi-core systems even without sparsity assumptions, and proposed a simplification of the recently introduced "perturbed iterate" framework that resolves it. Expand
Improved asynchronous parallel optimization analysis for stochastic incremental methods
TLDR
It is proved that ASAGA and KROMAGNON can obtain a theoretical linear speedup on multi-core systems even without sparsity assumptions, and the overlap constant is investigated, an ill-understood but central quantity for the theoretical analysis of asynchronous parallel algorithms. Expand
Multi-subject Dictionary Learning to Segment an Atlas of Brain Spontaneous Activity
TLDR
A new hierarchical probabilistic model for brain activity patterns that does not require an experimental design to be specified is given and this model is estimated in the dictionary learning framework, learning simultaneously latent spatial maps and the corresponding brain activity time-series. Expand
SymPy: Symbolic computing in Python
TLDR
The architecture of SymPy is presented, a description of its features, and a discussion of select domain specific submodules are discussed, and an emphasis is placed on extensibility and ease of use. Expand
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