-
IRMAR, CNRS.
- Rennes, France.
-
01:25
(UTC -12:00) - https://navaro.pages.math.cnrs.fr
- https://orcid.org/0000-0002-7372-3221
Starred repositories
Python Data Science Handbook: full text in Jupyter Notebooks
⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 or handson-mlp instead.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Notebooks and code for the book "Introduction to Machine Learning with Python"
this repository accompanies the book "Grokking Deep Learning"
An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
Jupyter notebooks from the scikit-learn video series
A python library for decision tree visualization and model interpretation.
A small library for automatical adjustment of text position in matplotlib plots to minimize overlaps.
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris
Koç University deep learning framework.
Numerical Tours of Signal Processing
Discussion of general issues related to the project.
A clean book theme for scientific explanations and documentation with Sphinx
A set of jupyter notebooks for the practice of TDA with the python Gudhi library together with popular machine learning and data sciences libraries.
Turn static HTML pages into live documents with Jupyter kernels.
A short course on Julia and open-source software development
Parallel computing in Python tutorial materials
GPU Programming with Julia - course at the Swiss National Supercomputing Centre (CSCS), ETH Zurich
This repository containts materials for End-to-End AI for Science
Parse and execute ipynb files in Sphinx
Tutorials on Julia topics
Material for the 2021 GPU workshop at JuliaCon
Supports de formation Deep Learning (diapos et exercices pratiques)
Repository which contains various scripts and work with various basketball statistics
Numerical simulations using flexible Lattice Boltzmann solvers
SciPy 2017 Pandas Tutorial
Resources for a 3.5 hour workshop on machine learning using the MLJ toolbox