baybe
awesome-experimental-standards-deep-learning
| baybe | awesome-experimental-standards-deep-learning | |
|---|---|---|
| 1 | 1 | |
| 185 | 25 | |
| 11.5% | - | |
| 9.9 | 2.0 | |
| about 10 hours ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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.
baybe
awesome-experimental-standards-deep-learning
-
[P] Introducing confidenceinterval, the long missing python library for computing confidence intervals
Very neat! I will add this to https://github.com/Kaleidophon/experimental-standards-deep-learning-research :-)
What are some alternatives?
modAL - A modular active learning framework for Python
yacs - YACS -- Yet Another Configuration System
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.
awesome-deep-learning-music - List of articles related to deep learning applied to music
vizier - Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
PyForecastTools - Forecast Verification/Validation Tools in Python
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
awesome-recruitment - List of my favourite recruitment things 💫
LiDAR-Guide - LiDAR Guide
orion - Asynchronous Distributed Hyperparameter Optimization.
software-papers - 📚 A curated list of papers for Software Engineers