garrascobike-core
Spotify_Song_Recommender
garrascobike-core | Spotify_Song_Recommender | |
---|---|---|
1 | 3 | |
1 | 28 | |
- | - | |
0.0 | 0.0 | |
about 2 years ago | almost 2 years ago | |
Jupyter Notebook | Jupyter Notebook | |
GNU General Public License v3.0 only | MIT License |
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garrascobike-core
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Garrascobike: bike recommendation system Web App
garrascobike-core - code to build the recommendation system
Spotify_Song_Recommender
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Spotify Song Recommender that uses Data Science Modeling
You can find the github project Here. To run the code, download the notebook file (.ipynb) and load it in google colab. Once you have it loaded, there are step by step instructions in the notebook. The code is pretty easy to run and just requires some link copy and pasting, so I would so programming experience is not required.
- Spotify Song Recommender
What are some alternatives?
YPDL-Build-a-movie-recommendation-engine-with-TensorFlow - In this tutorial, we are going to build a Restricted Boltzmann Machine using TensorFlow that will give us recommendations based on movies that have been watched already. The datasets we are going to use are acquired from GroupLens and contains movies, users, and movie ratings by these users.
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
PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks
handson-ml - ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
subreddit-text-downloader - Download subreddit comments
Bayesian-Optimization-in-FSharp - Bayesian Optimization via Gaussian Processes in F#
garrascobike-fe - Front-end code of the Garrascobike project
mango - Parallel Hyperparameter Tuning in Python
StravaKudos - :running: :dart: Predicting Strava Kudos on my own activities using the given activity's attributes.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
feature-engineering-tutorials - Data Science Feature Engineering and Selection Tutorials
Hyperactive - An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.