Hyperactive
anovos
| Hyperactive | anovos | |
|---|---|---|
| 8 | 1 | |
| 490 | 77 | |
| - | - | |
| 7.7 | 0.0 | |
| 5 months ago | about 1 year ago | |
| Python | Jupyter Notebook | |
| MIT License | GNU General Public License v3.0 or later |
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.
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.
anovos
What are some alternatives?
mango - Parallel Hyperparameter Tuning in Python
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
pybobyqa - Python-based Derivative-Free Optimization with Bound Constraints
Apache-Spark-Guide - Apache Spark Guide
opytimizer - 🐦 Opytimizer is a Python library consisting of meta-heuristic optimization algorithms.
feast - Feature Store for Machine Learning
OpenMetadata - Open Standard for Metadata. A Single place to Discover, Collaborate and Get your data right.
project-atlas-sao-paulo - A project for the development of rich geospatial data from the city of São Paulo for use in Machine Learning models.
optuna-examples - Examples for https://github.com/optuna/optuna
pyspark-tutorial - PySpark Tutorial for Beginners - Practical Examples in Jupyter Notebook with Spark version 3.4.1. The tutorial covers various topics like Spark Introduction, Spark Installation, Spark RDD Transformations and Actions, Spark DataFrame, Spark SQL, and more. It is completely free on YouTube and is beginner-friendly without any prerequisites.
optimization-tutorial - Tutorials for the optimization techniques used in Gradient-Free-Optimizers and Hyperactive.
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.