¯\_(ツ)_/¯ Patrick's repositories
docker_memo
Useful notes when using Docker
Inspiring-projects-with-positive-impacts
Gathering great projects that have positive impacts on our society
awesome-data-engineering
A curated list of data engineering tools for software developers
awesome-machine-learning-interpretability
A curated list of awesome machine learning interpretability resources.
cheatsheet-gcp-A4
GCP: gcloud, gsutil, etc.
cheatsheet-kubernetes-A4
:book: Kubernetes CheatSheets In A4
cookiecutter
A command-line utility that creates projects from cookiecutters (project templates). E.g. Python package projects, jQuery plugin projects.
cookiecutter-data-science
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
FlockingBehaviour-Swarm
Drones working together for collective movement and flocking behaviour written in ROS with python nodes (thesis project Participants: Kevin Gutierrez, Celeste Villaverde, Mariano Orozco).
GraphQL_memo
my memo for GraphQL
Guerledan_SAILBOAT_2019
Intelligence artificielle pour la cartographie GPS - Un repo git pour les codes en C++ et Python de simulation du voilier Brave. Basé sur ENSTA Bretagne Robotics
interpretable_machine_learning_with_python
Practical techniques for interpreting machine learning models.
learntools
Tools and tests used in Kaggle Learn exercises
mli-resources
H2O.ai Machine Learning Interpretability Resources
next18-ai-in-motion
Sample code for the AI in Motion demo
pseudonymization_service_with_GCP
Using Google Cloud DLP (Data Loss Prevention) in order to anonymize/pseudonymize sensitive data in Google Cloud
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
Smart-Mirror
Raspberry powered mirror which can display news, weather, calendar events
three.js
JavaScript 3D library.
training-data-analyst
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
WebFundamentals
Best practices for modern web development
word-embedding-dimensionality-selection
On the Dimensionality of Word Embedding