mahmoudsegni Goto Github PK

archerycalculator

Code for the web app archerycalculator.co.uk providing archery handicaps and classifications

coding-interview-university

A complete computer science study plan to become a software engineer.

data_structures

In this repo I will be sharing my leaning path to mastering the basics of data structures

deep-multimodal-subspace-clustering-networks

Tensorflow implementation of "Deep Multimodal Subspace Clustering Networks"

fastapi_polls_project

flask-api-for-audio-emotion-recognition

gan_tuto

iemocap-data

image_classification

library-system

library system using python.

magface

MagFace: A Universal Representation for Face Recognition and Quality Assessment, CVPR2021, Oral

mahmoud-segni

mahmoudsegni

make-your-first-pull-request

A repository to help you start your open source journey! 💫

problem-sloving

social-media-sentiment-analysis-for-tunisian-arabizi

On social media, Arabic speakers tend to express themselves in their own local dialect. To do so, Tunisians use ‘Tunisian Arabizi’, where the Latin alphabet is supplemented with numbers. However, annotated datasets for Arabizi are limited; in fact, this challenge uses the only known Tunisian Arabizi dataset in existence. Sentiment analysis relies on multiple word senses and cultural knowledge, and can be influenced by age, gender and socio-economic status.For this task, we have collected and annotated sentences from different social media platforms. The objective of this challenge is to, given a sentence, classify whether the sentence is of positive, negative, or neutral sentiment. For messages conveying both a positive and negative sentiment, whichever is the stronger sentiment should be chosen. Predict if the text would be considered positive, negative, or neutral (for an average user). This is a binary task.

speech-emotion-recognition

speech-emotion-recognition-training

summary-using-bert

test