madvid

Matthieu DAVID's repositories

42_ml_modules

[42 curriculum] One week to learn basics in Machine Learning! 🤖 (well, as one of the authors, It was just to get the XP because the pedago of 42Paris was too slow to give it to me). This project is a Machine Learning bootcamp created by 42 AI.

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42_Gomoku

Gomoku is a game which is played on Go board. The objectives of the project is to develop an agent capable to beat human player. Across the project we tackle minimax algo, heuristic function, optimization and user interface.

Language:PythonMIT2 10

PhD_influence_HCN_V_defect_solubility_diffusion_O_in_Ni

Part of my work realized during the years as a PhD student. I was in the team MEMO of the CIRIMAT lab in Toulouse. I study via a multi-scale approach the influence influence of H, C, N impurities and point defects on the solubility and diffusion of the oxygen in the nickel (fcc phase). My works was under the supervision of Damien Connétable and Daniel Monceau.

GPL-3.02 10

42_DSLR

Basic project about logistic regression. The goal is to implement from scratch a One vs All model. [Machine Learning] + [Supervised Learning] + [One vs All model]

Language:Jupyter NotebookMIT1 10

42_FdF

Projet graphique permettant de représenter le maillage en fils de fer d'un terrain en 3D. Graphical project. The program represents the wire mesh of 3D map.

Language:CGPL-3.0100

benchmark_AJ-Effisciences

Project realised during the hackathon organised by Aligment Jams hosted by Cooperative AI Foundation and Apart Research with Lewis Hammond and Esben Kran.

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madvid

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Tern_Color_Scale

Once uppon a time, I needed a ternary colorscale. Inspired by https://github.com/jschoeley/tricolore, here a python code to get a tricolor scale in Python.

Language:PythonGPL-3.0100

42_fractol

Ce projet consiste à créer un petit logiciel d’exploration fractale.

Language:Jupyter NotebookNOASSERTION000

DL_specialization_standford

Notes and certificates about the MOOC Deep learning specialization of Standford / Deeplearning.ai

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42_computorv1

[42 curriculum] Polynomial equation explicit resolution solver. For zeroth, first and second order polynomial equation (+ third order as a bonus) program solver.

Language:PythonMIT000

42_Corewar

"In this project, you will create a virtual “arena” in which programs will fight against one another (the “Champions”). You will also create an assembler to compile those Champions as well as a Champion to show the world that you can create life from coffee." [from 42 subject]

Language:CGPL-3.001 2

42_ft_linear_regression

(42 project) This project will be your first steps into AI and Machine Learning. You're going to start with a simple, basic machine learning algorithm. You will have to create a program that predicts the price of a car by using a linear function train with a gradient descent algorithm.

Language:PythonGPL-3.0000

42_Internship_1

In the 42 curriculum we have the opportunity to perform one (or two) internship. This repository contains a part of the work realised during the 6 months in Natixis Bank in the Antifraud team.

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42_wolf3d

Inspired by Wolfenstein famous game from the 90's. The goal is to be able to represent a dynamic view inside a maze using ray-casting method.

Language:CMIT010

devinterp

Tools for studying developmental interpretability in neural networks.

Language:Python000

discovering_latent_knowledge

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Discovering_Latent_Knowledge_in_Language_Models

Based one the paper "Discovering Latent Knowledge in Language Models without supervision" this project discuss ...

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Goal_Visu_RL

Reimplementation of the interpretability tools describe in **Goal Misgeneralization in Deep Reinforcement Learning** (https://arxiv.org/pdf/2105.14111.pdf)

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hackathon42

Subject of the hackathon 42

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Master2_internship

Modélisation ab-initio des processus de diffusion lors de la croissance des films minces de nitrures métalliques: TiN et AlN

GPL-3.0010

ml_module_00

Language:Python000

ml_module_01

[42 curriculum] The goal of this module is to get started with the basics of linear regression. You will study, in the field of machine learning, what we call an hypothesis, cost function, gradient descent and some notions of feature scaling.

Language:Jupyter Notebook000