Showing results for System theme Vector Vector
GitHub Repo
https://github.com/NadaHaimn/Move-recommendation-system
NadaHaimn/Move-recommendation-system
Intelligent Movie Recommendation System ,This project is a sophisticated content-based recommendation engine that suggests movies based on semantic similarity. By analyzing movie metadata with TF-IDF vectorization and cosine similarity algorithms, it identifies films with comparable themes, styles, and narratives.
GitHub Repo
https://github.com/kbnim/elte-fi-opsys-assignment
kbnim/elte-fi-opsys-assignment
Easter-themed assignment [Course: Operating Systems]
GitHub Repo
https://github.com/aryandas2911/Next-Arc
aryandas2911/Next-Arc
A content-based anime recommendation system using TF-IDF vectorization and cosine similarity over anime genres, tags, and themes. Deployed with Streamlit to help users discover their next anime based on what they already love.
GitHub Repo
https://github.com/atulhari/Tracking-Navigation-and-SLAM
atulhari/Tracking-Navigation-and-SLAM
The exercises are all part of a typical application theme, namely tracking, navigation and SLAM: • Bayesian estimation applied to beacon based measurement systems • Kinematic and dynamic models for tracking • Tracking based on discrete Kalman filtering for linear-Gaussian systems • Tracking with extended Kalman filtering in nonlinear systems • Tracking with particle filtering in nonlinear systems • Slam As such the exercises cover the following theoretical subjects: 1. Fundamentals of parameter estimation; static and scalar case 2. Unbiased linear minimum mean square estimation; static and scalar case 3. Unbiased linear minimum mean square estimation; static and vectorial case 4. Propagation of uncertainty in Gaussian-linear systems; prediction 5. Discrete Kalman filtering 6. Extended Kalman filtering 7. Particle filtering 8. SLAM
GitHub Repo
https://github.com/tdyer38072/Cat-Chat
tdyer38072/Cat-Chat
CAT CHAT v3.0 - Enterprise-grade local-first AI platform with advanced memory management, multi-AI integration (OpenAI/Claude), 60+ themes, plugin architecture, and real-time collaboration. Features sophisticated memory consolidation, vector search, persona system, and production-ready Docker deployment.
GitHub Repo
https://github.com/ThomasJButler/Morpheus
ThomasJButler/Morpheus
An intelligent document reasoning system with a Matrix-themed interface.
GitHub Repo
https://github.com/Vishal8944/Netflix-Movie-Recommendation-System
Vishal8944/Netflix-Movie-Recommendation-System
🎥 A simple content-based movie recommendation system built with Streamlit. 📊 Uses TF-IDF vectorization and cosine similarity to suggest similar movies based on genres. 🛠️ Powered by Python, pandas, and scikit-learn. 🎨 Features a sleek dark-themed user interface. 🚀 Easily customizable and perfect for beginners exploring recommender systems.
GitHub Repo
https://github.com/natashaoberoi/movie_qa_system
natashaoberoi/movie_qa_system
This project builds a question-answering system for a 10,000-movie IMDB dataset. It answers both semantic questions about themes and content and factual questions that require filtering or computation. Using vector search and an LLM, the system handles queries ranging from “alien movies” to “average rating of James Bond films.”
GitHub Repo
https://github.com/KrsnaYadav07/Anime-Recommendation
KrsnaYadav07/Anime-Recommendation
Anime recommendation system using content-based filtering. Utilizes genres, themes, synopsis, cast, and director info to create TF-IDF and Count_Vectorizer vectors, applying cosine similarity to generate top-5 anime recommendations.
GitHub Repo
https://github.com/bhagirath00/Movie-Recommendation-using-Semantic-Modeling