Ayoub ELma's repositories
Recsys_Nudging_Food
RecSys Nudging and Labeling
10DaysStatisticsChallenge_HackerRanck
Learning by doing, the statistics that evey dataist should now, provided by HackerRank
Amazon_fine_food_review
Amazon food review classification, from comments know the food taste
aws-machine-learning-university-accelerated-tab
Machine Learning University: Accelerated Tabular Data Class
Company_Employees_Decisoin
Use machine learinng to predict what employee are prone to leave the company
building-a-simple-recommendation-system-using-pytorch
Building a simple PyTorch Recommendation System
competitive-recsys
A collection of resources for Recommender Systems (RecSys)
computer-science
:mortar_board: Path to a free self-taught education in Computer Science!
ConversationalFoodbot
Chatbot to get healthy food options
Country_RS_analysis
Country RS analysis for reaserch purpose
d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 175 universities.
elliot
Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
ImageAttractivenessExp
Judge the attractiveness of food image
machine_learning_examples
A collection of machine learning examples and tutorials.
Master_thesis_Country_RS
Country Recommender system, this system will recommend best place that suits you profile :)
ML-From-Scratch
Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep learning.
ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
MMCFRS
Multi-modal Conversational Food Recommender System - KaRS 2022 ACM RecSys Workshop
natural-language-processing
Resources for "Natural Language Processing" Coursera course.
nlp_course
YSDA course in Natural Language Processing
Open-IE-Papers
Open Information Extraction (OpenIE) and Open Relation Extraction (ORE) papers and data.
Recipe-Image-Attractiveness
Judging Recipe Image Attractiveness
ThinkStats2
Text and supporting code for Think Stats, 2nd Edition
Two-Tower-Recommender-system-Pytorch
This project focuses on implementing a Two-Tower model to extract embeddings for users and recipes. These embeddings are then utilised to retrieve the most relevant recipes for a given user based on cosine similarity. The dataset used in this implementation can be requested through here: