awesome-experimental-standards-deep-learning VS baybe

Compare awesome-experimental-standards-deep-learning vs baybe and see what are their differences.

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awesome-experimental-standards-deep-learning baybe
1 1
25 179
- 10.1%
2.0 9.9
about 1 year ago 7 days ago
Python Python
GNU General Public License v3.0 only Apache License 2.0
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.

awesome-experimental-standards-deep-learning

Posts with mentions or reviews of awesome-experimental-standards-deep-learning. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-03-11.

baybe

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

What are some alternatives?

When comparing awesome-experimental-standards-deep-learning and baybe you can also consider the following projects:

yacs - YACS -- Yet Another Configuration System

modAL - A modular active learning framework for Python

awesome-deep-learning-music - List of articles related to deep learning applied to music

emukit - A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

PyForecastTools - Forecast Verification/Validation Tools in Python

vizier - Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.

awesome-recruitment - List of my favourite recruitment things 💫

nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

LiDAR-Guide - LiDAR Guide

orion - Asynchronous Distributed Hyperparameter Optimization.

software-papers - 📚 A curated list of papers for Software Engineers