Cours d'Algorithmique Appliquée avec Python pour BTS SIO SISR

Overview

Course: Introduction to Applied Algorithms with Python (in French)

This is the source code of the website for the Applied Algorithms with Python course targetting undergraduate students with a computer networks major. This course is written in French and targets more specifically BTS SIO SISR "Algorithme Appliquée".

The source code may be used as a template for creating other open source courses.

Technologies

The course uses Markdown extensively in the following areas:

  • Website: 11ty (Eleventy) and Nunjucks are used to generate the website, with Bootstrap 5 for the layout.
  • Slides: Marp is used to generate the slides and PDFs.
  • Exercises: Jupyter Notebook is used to generate the notebooks for the exercises and homework.

The website is deployed to GitHub Pages.

The source code was initially generated and adapted from the yhatt/marp-cli-example template.

Build

On a system with bash, the following script may be used to install the dependencies and setup the work environment:

bin/bootstrap.sh

Please note that npm is required. If not available, the bootstrap.sh script will try to install it with nvm, if found.

To build a local development version of the website that may be run on a local webserver, the following script is available:

bin/compile.sh

A local webserver that serves the local website and watches for changes may be run with:

bin/run.sh

Deploy

The yhatt/marp-cli-example template uses Continuous Integration and Continuous Deployment thanks to GitHub Actions. Whenever a Pull Request is merged to the master branch, the CI runs and deploys the website to the gh-pages branch. In turn, the GitHub Pages are automatically updaded with the content of this branch.

Known bugs

The following minor bugs are known and may be fixed later:

  • The run.sh script uses eleventy --serve and therefore does not watch for changes in CSS, assets or slides. You need to execute the compile.sh script to work around this limitation.
  • When the Eleventy local webserver is running, the Marp PDF generation from Markdown fails (apparently) due to some obscure puppeteer issue. This issue has not been investigated (yet). Rebooting allows working around the issue.

Contributions

If you speak both Python and French, and if you are willing to improve the course, please feel free to submit Pull Requests.

Owner
Loic Yvonnet
Self-Employed | Software Engineer | C++ Developer | 3DEXPERIENCE Application Architect
Loic Yvonnet
Individual Tree Crown classification on WorldView-2 Images using Autoencoder -- Group 9 Weak learners - Final Project (Machine Learning 2020 Course)

Created by Olga Sutyrina, Sarah Elemili, Abduragim Shtanchaev and Artur Bille Individual Tree Crown classification on WorldView-2 Images using Autoenc

2 Dec 08, 2022
scalingscattering

Scaling The Scattering Transform : Deep Hybrid Networks This repository contains the experiments found in the paper: https://arxiv.org/abs/1703.08961

Edouard Oyallon 78 Dec 21, 2022
Object detection (YOLO) with pytorch, OpenCV and python

Real Time Object/Face Detection Using YOLO-v3 This project implements a real time object and face detection using YOLO algorithm. You only look once,

1 Aug 04, 2022
Non-Vacuous Generalisation Bounds for Shallow Neural Networks

This package requires jax, tensorflow, and numpy. Either tensorflow or scikit-learn can be used for loading data. To run in a nix-shell with required

Felix Biggs 0 Feb 04, 2022
Code to reproduce the results for Statistically Robust Neural Network Classification, published in UAI 2021

Code to reproduce the results for Statistically Robust Neural Network Classification, published in UAI 2021

1 Jun 02, 2022
Framework web SnakeServer.

SnakeServer - Framework Web 🐍 Documentação oficial do framework SnakeServer. Conteúdo Sobre Como contribuir Enviar relatórios de segurança Pull reque

Jaedson Silva 0 Jul 21, 2022
A Graph Neural Network Tool for Recovering Dense Sub-graphs in Random Dense Graphs.

PYGON A Graph Neural Network Tool for Recovering Dense Sub-graphs in Random Dense Graphs. Installation This code requires to install and run the graph

Yoram Louzoun's Lab 0 Jun 25, 2021
Rule-based Customer Segmentation

Rule-based Customer Segmentation Business Problem A game company wants to create level-based new customer definitions (personas) by using some feature

Cem Çaluk 2 Jan 03, 2022
GraphGT: Machine Learning Datasets for Graph Generation and Transformation

GraphGT: Machine Learning Datasets for Graph Generation and Transformation Dataset Website | Paper Installation Using pip To install the core environm

y6q9 50 Aug 18, 2022
Implementation for paper "STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement" (ICCV 2021).

STAR-pytorch Implementation for paper "STAR: A Structure-aware Lightweight Transformer for Real-time Image Enhancement" (ICCV 2021). CVF (pdf) STAR-DC

43 Dec 21, 2022
Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022

PyCRE Conflict-aware Inference of Python Compatible Runtime Environments with Domain Knowledge Graph, ICSE 2022 Dependencies This project is developed

<a href=[email protected]"> 7 May 06, 2022
Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning

Repository for scripts and notebooks from the book: Programming PyTorch for Deep Learning

Ian Pointer 368 Dec 17, 2022
Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better performance.

InfoPro-Pytorch The Information Propagation algorithm for training deep networks with local supervision. (ICLR 2021) Revisiting Locally Supervised Lea

78 Dec 27, 2022
Technical Analysis library in pandas for backtesting algotrading and quantitative analysis

bta-lib - A pandas based Technical Analysis Library bta-lib is pandas based technical analysis library and part of the backtrader family. Links Main P

DRo 393 Dec 20, 2022
Code for ICLR 2021 Paper, "Anytime Sampling for Autoregressive Models via Ordered Autoencoding"

Anytime Autoregressive Model Anytime Sampling for Autoregressive Models via Ordered Autoencoding , ICLR 21 Yilun Xu, Yang Song, Sahaj Gara, Linyuan Go

Yilun Xu 22 Sep 08, 2022
Code, Models and Datasets for OpenViDial Dataset

OpenViDial This repo contains downloading instructions for the OpenViDial dataset in 《OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Vis

119 Dec 08, 2022
Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

Progressive Transformers for End-to-End Sign Language Production Source code for "Progressive Transformers for End-to-End Sign Language Production" (B

58 Dec 21, 2022
Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021)

Beyond Image to Depth: Improving Depth Prediction using Echoes (CVPR 2021) Kranti Kumar Parida, Siddharth Srivastava, Gaurav Sharma. We address the pr

Kranti Kumar Parida 33 Jun 27, 2022
Official PyTorch implementation of "Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics".

Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics This repository is the official PyTorch implementation of "Physics-aware Differ

USC-Melady 46 Nov 20, 2022
Imaginaire - NVIDIA's Deep Imagination Team's PyTorch Library

Imaginaire Docs | License | Installation | Model Zoo Imaginaire is a pytorch library that contains optimized implementation of several image and video

NVIDIA Research Projects 3.6k Dec 29, 2022