Julien Jerphanion (jjerphan)

jjerphan

Geek Repo

Company:@QuantStack

Location:Almost surely in a conda-forge feedstock

Home Page:jjerphan.xyz

Twitter:@jjerphan

Github PK Tool:Github PK Tool


Organizations
conda-forge
mamba-org
QuantStack
scientific-python
scikit-learn
scikit-learn-inria-fondation
soda-inria
xtensor-stack

Julien Jerphanion's repositories

bike_counters

Bike counters RAMP starting kit

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blog

Hosting the scikit-learn blog.

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dpctl

Data Parallel Control

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enhancement_proposals

Enhancement proposals for scikit-learn: structured discussions and rational for large additions and modifications

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EuroSciPy22

Repository for the EuroSciPy sprint

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Fingerprint_Image_Enhancement

C++ based fingerprint image enhancement using OpenCV

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follow-up

Weekly follow-up of Consortium development priorities

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HiPart

Hierarchical divisive clustering algorithm execution, visualization and Interactive visualization.

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ilastik

ilastik-shell, applets, and workflows to string them together.

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joblib_dask_deadlock

Minimal Reproducible Bug Example: dead lock in `Parallel` when using `dask` as a back-end

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kmeans_dpcpp

Implementation of KMeans algorithm using DPC++ following sklearn-numba-dpex

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labeler

An action for automatically labelling pull requests

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nilearn

Machine learning for NeuroImaging in Python

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pelican-katex

LaTeX pre-rendering for pelican

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per4m

Profiling and tracing information for Python using viztracer and perf, the GIL exposed.

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plantuml_docker

DockerFile for plantuml

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polars

Fast multi-threaded, hybrid-streaming DataFrame library in Rust | Python | Node.js

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pyo3

Rust bindings for the Python interpreter

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pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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ramp-docs

Documentation of the RAMP bundle

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scientific-python.org

Source code for the Scientific Python Ecosystem project page.

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scikit-learn-feedstock

A conda-smithy repository for scikit-learn.

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sklearn_benchmarks

Comparative benchmarking tool for scikit-learn's estimators

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supervised_missing

Code for the paper "On the consistency of supervised learning with missing values"

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