Topic: reccomendation-system Goto Github
Some thing interesting about reccomendation-system
Some thing interesting about reccomendation-system
reccomendation-system,Project for HackSC (The University of Southern California Hackathon)
User: amolbudhiraja
reccomendation-system,Personalized smoking recommendations based on Collaborative Filtering.
User: christakakis
reccomendation-system,Reading Recommendation System: This project implements K Nearest Neighbor (kNN) Collaborative Filtering to build a book recommender system based on a publicly available dataset.
User: cs-edwards
reccomendation-system,Building a Custom Vector Search Engine with Weaviate : The project discusses the architecture of Weaviate, an open-source vector database and provides a tutorial implementation of a custom vector search engine using Weaviate Cloud Service(WCS).
User: cs-edwards
reccomendation-system,Indian food reccomendor
User: d1b2
reccomendation-system,An overview of reccomendation systems in Python
User: dacker15
reccomendation-system,An overview of reccomendation systems in R
User: dacker15
reccomendation-system,🎵 Unlock the Future of Music with Predictive Analysis!
User: joextitan
reccomendation-system,A composition of Machine Learning Projects in python using algorithms in supervised, unsupervised, and deep learning.
User: luisosorio3214
reccomendation-system,CineSuggest," an advanced movie recommender powered by machine learning, removes uncertainty in film selection, employing data-rich algorithms for personalization.
User: nikhilpanda01
Home Page: https://cine-suggest-personalized-movie-suggestions.streamlit.app/
reccomendation-system,By using a dataset sourced from IMDb taken from the kaggle.com site. This system can provide video game recommendations based on their genre.
User: rezaafaisal
reccomendation-system,M.Sc. Courses in Data Science, including Machine Learning, Deep Learning, Statistics and Data Analysis, and Recommendation Systems.
User: sharonmordechai
reccomendation-system,Exploring Bloom embeddings as a compression technique for recommendation algorithms. Aimed at reducing the size of large input and output dimensionalities to enhance training and deployment efficiency on devices with limited hardware. This project evaluates Bloom embeddings using various hash functions and compares them with alternative methods.
User: sharonmordechai
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