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
27 304
- 7.9%
2.0 9.9
almost 2 years 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:

LiDAR-Guide - LiDAR Guide

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.

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

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

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

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

orion - Asynchronous Distributed Hyperparameter Optimization.

awesome-recruitment - List of my favourite recruitment things 💫

PyForecastTools - Forecast Verification/Validation Tools in Python

confidenceinterval - The long missing library for python confidence intervals

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Nutrient – The #1 PDF SDK Library, trusted by 10K+ developers
Other PDF SDKs promise a lot - then break. Laggy scrolling, poor mobile UX, tons of bugs, and lack of support cost you endless frustrations. Nutrient’s SDK handles billion-page workloads - so you don’t have to debug PDFs. Used by ~1 billion end users in more than 150 different countries.
www.nutrient.io
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