Artificial-Intelligence-Projects VS MovieRecommender

Compare Artificial-Intelligence-Projects vs MovieRecommender and see what are their differences.

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Artificial-Intelligence-Projects MovieRecommender
1 4
309 32
- -
2.6 0.0
11 months ago over 1 year ago
Jupyter Notebook Jupyter Notebook
- MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Artificial-Intelligence-Projects

Posts with mentions or reviews of Artificial-Intelligence-Projects. We have used some of these posts to build our list of alternatives and similar projects.

MovieRecommender

Posts with mentions or reviews of MovieRecommender. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing Artificial-Intelligence-Projects and MovieRecommender you can also consider the following projects:

lolesports-predictor - Personal machine learning & GUI project to predict League of Legends Esports game results between two teams

tutorials - AI-related tutorials. Access any of them for free → https://towardsai.net/editorial

TIMDB - The Indian Movie Database - supports content-based and collaborative filtering techniques

apartment_recommender_streamlit_app - Streamlit App that recommends apartments in Seattle using the Airbnb kaggle dataset: https://www.kaggle.com/code/rdaldian/airbnb-content-based-recommendation-system/data?select=listings.csv

TensorRec - A TensorFlow recommendation algorithm and framework in Python.

Machine-Learning-Specialization-Coursera - Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

recommenders - Best Practices on Recommendation Systems