Pinned Repositories
Application-of-convolutional-neural-network-in-chess-evaluation
In a game of chess, each move is made according to astrategy that aims to optimize the situation of the activeplayer. The evaluation of the position of the differentpieces is therefore a central issue that we will be tack-ling in this project. The goal we fixed ourselves is touse an evaluation of the board positions through a con-volutional neural network (CNN). This evaluation aimsto mimic the complex heuristic of the well known chessengineStockfish. We will then use this learned evaluationas the heuristic in aminimaxalgorithm to implement ourown chess AI.
billy-webpage-scrum
billyAI
ChainYo
GitHub profile
Coffee-Machine
High-dimensional-data-analysis
These projects were carried out as part of the MATH2021-1 High-dimensional data analysis course of the ULiege.
large-scale-data-systems
In order to safely shepherd a rocket to a circular orbit of about 100 km, it is imperative to ensure a good consistency and availability of the computing resources used to guide the rocket. One way to achieve this is to rely on a cluster of computers collaborating to guide the rocket, using distributed algorithms. Our implementation will leverage a modification of the Raft consensus algorithm
numerical-optimization
Compressive sensing exploit the possibility to represent an image with a sparse representation.
PI
Modeling epidemic using bayesian Bayesian inference and SEIR+ model
Privacy-in-household-robots
Privacy-in-household-robots
jhubar's Repositories
jhubar/PI
Modeling epidemic using bayesian Bayesian inference and SEIR+ model
jhubar/Application-of-convolutional-neural-network-in-chess-evaluation
In a game of chess, each move is made according to astrategy that aims to optimize the situation of the activeplayer. The evaluation of the position of the differentpieces is therefore a central issue that we will be tack-ling in this project. The goal we fixed ourselves is touse an evaluation of the board positions through a con-volutional neural network (CNN). This evaluation aimsto mimic the complex heuristic of the well known chessengineStockfish. We will then use this learned evaluationas the heuristic in aminimaxalgorithm to implement ourown chess AI.
jhubar/billy-webpage-scrum
jhubar/billyAI
jhubar/ChainYo
GitHub profile
jhubar/High-dimensional-data-analysis
These projects were carried out as part of the MATH2021-1 High-dimensional data analysis course of the ULiege.
jhubar/large-scale-data-systems
In order to safely shepherd a rocket to a circular orbit of about 100 km, it is imperative to ensure a good consistency and availability of the computing resources used to guide the rocket. One way to achieve this is to rely on a cluster of computers collaborating to guide the rocket, using distributed algorithms. Our implementation will leverage a modification of the Raft consensus algorithm
jhubar/numerical-optimization
Compressive sensing exploit the possibility to represent an image with a sparse representation.
jhubar/Privacy-in-household-robots
Privacy-in-household-robots
jhubar/edge_detection
This is a flutter plugin to detect edges in a live camera, take the picture of detected edges object, crop it, and save.
jhubar/Engagement-de-confidentialite
jhubar/exercice-python
jhubar/gdc
jhubar/info8010-deep-learning
Lectures for INFO8010 - Deep Learning, ULiège
jhubar/invoiceProcessing
jhubar/jhubar
jhubar/jhubar.github.io
This my resume code source
jhubar/lafermeliegeoise
jhubar/lvdpdc
jhubar/maison
jhubar/markdown-documentation-template
jhubar/master-thesis
jhubar/pur_architecture
jhubar/SemanticData
jhubar/sentiment_analysis
jhubar/Signal
jhubar/TIC
Information theory provides a quantitative measure of the information provided by a message or an observation. This notion was introduced by Claude Shannon in 1948 in order to establish the limits of what is possible in terms of data compression and transmission over noisy channels. Since these times, this theory has found many applications in telecommunications, computer science ans statistics. The course is composed of three parts
jhubar/transformers
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
jhubar/unilm
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
jhubar/web-and-text