Spotify_Song_Recommender
Hyperactive
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| Spotify_Song_Recommender | Hyperactive | |
|---|---|---|
| 3 | 8 | |
| 28 | 487 | |
| - | - | |
| 0.0 | 7.7 | |
| almost 2 years ago | 4 months ago | |
| Jupyter Notebook | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
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
Hyperactive
- Hyperactive Version 4.5 Released
- Hyperactive: An optimization and data collection toolbox for AutoML
- Hyperactive: Optimize computationally expensive models with powerful algorithms
- Show HN: Hyperactive – A highly versatile AutoML Toolbox
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Hyperactive – Easy Neural Architecture Search for Deep Learning in Python
Check out the Neural Architecture Search Tutorial here: https://nbviewer.jupyter.org/github/SimonBlanke/hyperactive-...
Neural Architecture Search is just one of many optimization applications you can work on with Hyperactive. Check out the examples in the official github repository: https://github.com/SimonBlanke/Hyperactive/tree/master/examp...
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Gradient-Free-Optimizers A collection of modern optimization methods in Python
Gradient-Free-Optimizers is a lightweight optimization package that serves as a backend for Hyperactive: https://github.com/SimonBlanke/Hyperactive
Hyperactive can do parallel computing with multiprocessing or joblib, or a custom wrapper-function.
What are some alternatives?
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
mango - Parallel Hyperparameter Tuning in Python
handson-ml - ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.
pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints
Bayesian-Optimization-in-FSharp - Bayesian Optimization via Gaussian Processes in F#
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
optuna-examples - Examples for https://github.com/optuna/optuna
StravaKudos - :running: :dart: Predicting Strava Kudos on my own activities using the given activity's attributes.
OpenMetadata - Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.