francoislegac

algorithms

algorithm_exercices_leet_code

avito-kaggle

brain_tumor_classif

Classification of MRI scan to define if an image contains a brain tumor or not.

brain_tumor_classification_segmentation

We try to discriminate healthy scans from patients suffering from brain tumor. We then detect the tumors using U-Net, the semantic segmentation model .

datascience_projects

This repository contains two projects of supervised classification. The first one is a credit scoring prediction problem for a bank. The second one is a customer satisfaction score prediction

deep_learning

Exercises from the course cs231n : visual recognition from Stanford. Implementation of FC NN and CNN (~from scratch using numpy) and using TensorFlow

JupyterNotebookImageExporter

machine_learning_M2

Practical sessions from the course Data Mining (PCA, Linear Reg, KMeans, Gaussian Mixture, Logistic Reg, Bagging, Boosting, RF, NN ...)

MonteCarloTreeSearch_project_M2

Implementation of the game 'Connect 4' (Puissance 4) and parallelisation of the MCTS algorithm in C#

svm_functional_data

Use of SVM for functional data : spectrometric samples of octane. Based on the following paper : https://hal.archives-ouvertes.fr/hal-00144141/document

francoislegac/JupyterNotebookImageExporter

francoislegac/svm_functional_data

Use of SVM for functional data : spectrometric samples of octane. Based on the following paper : https://hal.archives-ouvertes.fr/hal-00144141/document

francoislegac/algorithms

algorithm_exercices_leet_code

francoislegac/avito-kaggle

francoislegac/brain_tumor_classif

Classification of MRI scan to define if an image contains a brain tumor or not.

francoislegac/brain_tumor_classification_segmentation

We try to discriminate healthy scans from patients suffering from brain tumor. We then detect the tumors using U-Net, the semantic segmentation model .

francoislegac/datascience_projects

This repository contains two projects of supervised classification. The first one is a credit scoring prediction problem for a bank. The second one is a customer satisfaction score prediction

francoislegac/deep_learning

Exercises from the course cs231n : visual recognition from Stanford. Implementation of FC NN and CNN (~from scratch using numpy) and using TensorFlow

francoislegac/build-day-trading-algo-and-deploy

francoislegac/cloud_run_repo

francoislegac/cluster-icm

Test parallele processing using ICM clusters

francoislegac/data_wrangling

Data Wrangling exercises using pandas

francoislegac/dummy_process_mining

A quick training working with dummy log data

francoislegac/eeg_visualization_kaggle

francoislegac/efficientnet

Implementation of EfficientNet model. Keras and TensorFlow Keras.

francoislegac/flaskapp_tuto

Creation of a web app using Flask

francoislegac/FoodSeg103-Benchmark-v1

MM'21 Main-Track paper

francoislegac/gpt-2xy

GPT-2 User Interface based on HuggingFace's Pytorch Implementation

francoislegac/kanedama

Take home test to join us :)

francoislegac/KNN_project_M1

A presentation of the KNN algorithm coded from scratch. The goal was to reduce the complexity of the algorithm, compare the different distances and choose the best number of neighbours using cross validation

francoislegac/map-ssr

francoislegac/mne-tuto

francoislegac/neural_network_project

Implementation of a FC Neural Network using numpy on CIFAR-10 ds

francoislegac/process_mining_101

Process mining tutorial

francoislegac/python_M2

francoislegac/stockscreen_api

francoislegac/tuto-debugging

francoislegac/tuto-packaging

francoislegac/tuto_calcul_distribue

francoislegac/tuto_pandas_json_normalize

Tutorial of the various uses of the pandas function : json_normalize()