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Hackathon de l’ENSAE
Hackathon ENSAE / La Croix-Rouge / DataForGood / Microsoft - 2015
Données de la Croix-Rouge
La Croix-Rouge, Données et défis
Getting started
Upload data
Database Schemas
Download data from Azure
Clean, process dates in text files
Cheat Sheet on Graphs
Cheat Sheet on files
Cheat Sheet on dates
Pip install from a notebook
Hackathon ENSAE / La Croix-Rouge / Crésus / Genius / Ernst & Young - 2016
Cheat Sheet on Graphs
Cheat Sheet on files
Cheat Sheet on dates
Pip install from a notebook
Hackathon ENSAE / Label Emmaüs / Latitudes / Genius / Ernst & Young - 2017
Cheat Sheet on Graphs
Cheat Sheet on files
Cheat Sheet on dates
Pip install from a notebook
Hackathon ENSAE / BRGM / Microdon / Latitudes / Genius / Ernst & Young - 2018
Uploader vos résultats
Eléments de code pour le challenge 2018
Défis
Challenges
City Tour
Shortest city tour
Longer city tours
Walk through all streets in a city
Shortest city tour (solution)
Longer city tours (solution)
City Bike
Chicago
City Bike Challenge
City Bike Views
Chicago
Ideas on City Bike Challenge
Bike Pattern
Bike Pattern 2
Nearest Neighours and k-d tree
Nearest Neighbours and Sparse Features
Coding Problems
Dés en séquences
Antisèches et exemples
API
Data Manipulation
Hackathon Helpers
Passwords
Useful functions
Cheat sheets
Cheat Sheet on Git
Uncommon operation with dataframes
Cheat Sheet on Graphs
Cheat sheet on Geocoordinates
Images and matrices
Cheat Sheet on files
Cheat Sheet on dates
Pip install from a notebook
Cheat Sheet on HTML
Examples on machine learning
PCA (Principal Component Analysis)
OnlineNewPopularity (data from UCI)
Image to features
End to end examples
Search engines for images through a REST API
Notebook Gallery
Bike Pattern
Bike Pattern 2
Chicago
Chicago
City Bike Challenge
City Bike Views
Ideas on City Bike Challenge
Seattle
Longer city tours
Longer city tours (solution)
Shortest city tour
Shortest city tour (solution)
Walk through all streets in a city
Nearest Neighbours and Sparse Features
2A.ml - Déterminer la vitesse moyenne des vélib
Cheat Sheet on Graphs
Cheat Sheet on HTML
Cheat Sheet on dates
Cheat Sheet on files
Cheat sheet on Geocoordinates
Image to features
Images and matrices
Pip install from a notebook
Uncommon operation with dataframes
Dés en séquences
Clean, process dates in text files
Database Schemas
Download data from Azure
Times Series
Upload data
Données INSEE
Exemple pour reconnaissance des inondations
Image et doublons
Image et doublons
OnlineNewPopularity (data from UCI)
PCA (Principal Component Analysis)
Notebooks Coverage
Blog Gallery
blog 0
blog 1
blog 2
blog 3
blog 4
blog 5
blog 6
FAQ, notebooks
FAQ
Bouts de code en cas de besoin
Magic commands
README
Changes
blog page - 1/1
License
Glossaire
Table Of Contents
Hackathon de l’ENSAE
Hackathon ENSAE / La Croix-Rouge / DataForGood / Microsoft - 2015
Données de la Croix-Rouge
La Croix-Rouge, Données et défis
Getting started
Upload data
Database Schemas
Download data from Azure
Clean, process dates in text files
Cheat Sheet on Graphs
Cheat Sheet on files
Cheat Sheet on dates
Pip install from a notebook
Hackathon ENSAE / La Croix-Rouge / Crésus / Genius / Ernst & Young - 2016
Cheat Sheet on Graphs
Cheat Sheet on files
Cheat Sheet on dates
Pip install from a notebook
Hackathon ENSAE / Label Emmaüs / Latitudes / Genius / Ernst & Young - 2017
Cheat Sheet on Graphs
Cheat Sheet on files
Cheat Sheet on dates
Pip install from a notebook
Hackathon ENSAE / BRGM / Microdon / Latitudes / Genius / Ernst & Young - 2018
Uploader vos résultats
Eléments de code pour le challenge 2018
Défis
Challenges
City Tour
Shortest city tour
Longer city tours
Walk through all streets in a city
Shortest city tour (solution)
Longer city tours (solution)
City Bike
Chicago
City Bike Challenge
City Bike Views
Chicago
Ideas on City Bike Challenge
Bike Pattern
Bike Pattern 2
Nearest Neighours and k-d tree
Nearest Neighbours and Sparse Features
Coding Problems
Dés en séquences
Antisèches et exemples
API
Data Manipulation
Hackathon Helpers
Passwords
Useful functions
Cheat sheets
Cheat Sheet on Git
Uncommon operation with dataframes
Cheat Sheet on Graphs
Cheat sheet on Geocoordinates
Images and matrices
Cheat Sheet on files
Cheat Sheet on dates
Pip install from a notebook
Cheat Sheet on HTML
Examples on machine learning
PCA (Principal Component Analysis)
OnlineNewPopularity (data from UCI)
Image to features
End to end examples
Search engines for images through a REST API
Notebook Gallery
Bike Pattern
Bike Pattern 2
Chicago
Chicago
City Bike Challenge
City Bike Views
Ideas on City Bike Challenge
Seattle
Longer city tours
Longer city tours (solution)
Shortest city tour
Shortest city tour (solution)
Walk through all streets in a city
Nearest Neighbours and Sparse Features
2A.ml - Déterminer la vitesse moyenne des vélib
Cheat Sheet on Graphs
Cheat Sheet on HTML
Cheat Sheet on dates
Cheat Sheet on files
Cheat sheet on Geocoordinates
Image to features
Images and matrices
Pip install from a notebook
Uncommon operation with dataframes
Dés en séquences
Clean, process dates in text files
Database Schemas
Download data from Azure
Times Series
Upload data
Données INSEE
Exemple pour reconnaissance des inondations
Image et doublons
Image et doublons
OnlineNewPopularity (data from UCI)
PCA (Principal Component Analysis)
Notebooks Coverage
Blog Gallery
blog 0
blog 1
blog 2
blog 3
blog 4
blog 5
blog 6
FAQ, notebooks
FAQ
Bouts de code en cas de besoin
Magic commands
README
Changes
blog page - 1/1
License
Glossaire
API
¶
Data Manipulation
Hackathon Helpers
Passwords
Useful functions
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