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  • Hackathon de l’ENSAE
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Plots multiple images in one graph

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Search engines for images through a REST API

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On this page
  • API REST
  • Challenges
    • Guess working and living areas in a city
    • Optimize a route
    • k-Nearest Neighbours and Sparse features
    • Trajectoires de vélib
  • Cheat Sheets
  • Coding Problems
  • hackathon_2015
  • Materials for the ENSAE Hackathon 2018
  • hackathon_2022
  • Premier pas en machine learning

Notebook Gallery#

Notebooks Coverage

  • API REST

  • Challenges

  • Cheat Sheets

  • Coding Problems

  • hackathon_2015

  • Materials for the ENSAE Hackathon 2018

  • hackathon_2022

  • Premier pas en machine learning

API REST#

A few notebooks on REST API.

  • Search engines for images through a REST API
_images/rest_api_search_images.thumb.png

Search engines for images through a REST API#

Challenges#

The following notebook introduce materials, explanation for challenge about algorithmic or data.

  • Guess working and living areas in a city

  • Optimize a route

  • k-Nearest Neighbours and Sparse features

  • Trajectoires de vélib

Guess working and living areas in a city#

Shared bicycles are available in almost every big city around the world. The data about available bicycles or trips are usually open. These notebooks show some ways to collect and use this data.

  • Bike Pattern
  • Bike Pattern 2
  • Chicago
  • Chicago
  • City Bike Challenge
  • City Bike Views
  • Ideas on City Bike Challenge
  • Seattle
_images/city_bike_solution_cluster.thumb.png

Bike Pattern#

_images/city_bike_solution_cluster_start.thumb.png

Bike Pattern 2#

_images/bike_chicago.thumb.png

Chicago#

_images/business_chicago.thumb.png

Chicago#

_images/city_bike_challenge.thumb.png

City Bike Challenge#

_images/city_bike_views.thumb.png

City Bike Views#

_images/city_bike_solution.thumb.png

Ideas on City Bike Challenge#

_images/bike_seatle.thumb.png

Seattle#

Optimize a route#

What is the shortest path going through a set of streets in a city? You will find some tips about the answer among the following notebook.

  • Longer city tours
  • Longer city tours (solution)
  • Shortest city tour
  • Shortest city tour (solution)
  • Walk through all streets in a city
_images/city_tour_long.thumb.png

Longer city tours#

_images/city_tour_long_solution.thumb.png

Longer city tours (solution)#

_images/city_tour_1.thumb.png

Shortest city tour#

_images/city_tour_1_solution.thumb.png

Shortest city tour (solution)#

_images/city_tour_data_preparation.thumb.png

Walk through all streets in a city#

k-Nearest Neighbours and Sparse features#

This a kind of mathematical puzzle which happens in a machine learning problem. Thatt riddle shows why sometimes it is quite helpful to understand a little bit of the mathematics behind the scenes.

  • Nearest Neighbours and Sparse Features
_images/nearest_neighbours_sparse_features.thumb.png

Nearest Neighbours and Sparse Features#

Trajectoires de vélib#

Le système vélib permet de connaître l’état des stations à intervalles réguliers. Ces données permettent-elles d’estimer la vitesse moyenne des cyclistes utilisant ce moyen de locomotion ? Que peut-on imaginer pour calculer un estimateur de cette vitesse ?

  • 2A.ml - Déterminer la vitesse moyenne des vélib
_images/velib_trajectories.thumb.png

2A.ml - Déterminer la vitesse moyenne des vélib#

Cheat Sheets#

Tips, tricks, tweaks about anything.

  • 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
_images/chsh_graphs.thumb.png

Cheat Sheet on Graphs#

_images/chsh_html.thumb.png

Cheat Sheet on HTML#

_images/chsh_dates.thumb.png

Cheat Sheet on dates#

_images/chsh_files.thumb.png

Cheat Sheet on files#

_images/chsh_geo.thumb.png

Cheat sheet on Geocoordinates#

_images/image_features.thumb.png

Image to features#

_images/chsh_images.thumb.png

Images and matrices#

_images/chsh_pip_install.thumb.png

Pip install from a notebook#

_images/chsh_pandas.thumb.png

Uncommon operation with dataframes#

Coding Problems#

Enigma, coding problems, exercises pour interviews…

  • Dés en séquences
_images/dices_sequence.thumb.png

Dés en séquences#

hackathon_2015#

  • Clean, process dates in text files
  • Database Schemas
  • Download data from Azure
  • Times Series
  • Upload data
_images/process_clean_files.thumb.png

Clean, process dates in text files#

_images/database_schemas.thumb.png

Database Schemas#

_images/download_data_azure.thumb.png

Download data from Azure#

_images/times_series.thumb.png

Times Series#

_images/upload_donnees.thumb.png

Upload data#

Materials for the ENSAE Hackathon 2018#

See more about this hackathon at Hackathon ENSAE / BRGM / Microdon / Latitudes / Genius / Ernst & Young - 2018.

  • Données INSEE
  • Exemple pour reconnaissance des inondations
  • Image et doublons
  • Récupération d’images avec Bing
_images/donnees_insee.thumb.png

Données INSEE#

_images/baseline_images_keras.thumb.png

Exemple pour reconnaissance des inondations#

_images/images_dups.thumb.png

Image et doublons#

_images/images_gets.thumb.png

Récupération d’images avec Bing#

hackathon_2022#

  • Son
_images/traitement_du_son.thumb.png

Son#

Premier pas en machine learning#

Quelques idées simples pour démarrer avec des données.

  • OnlineNewPopularity (data from UCI)
  • PCA (Principal Component Analysis)
_images/online_news_popylarity.thumb.png

OnlineNewPopularity (data from UCI)#

_images/PCA.thumb.png

PCA (Principal Component Analysis)#

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