awesome-experimental-standards-deep-learning
Repository collecting resources and best practices to improve experimental rigour in deep learning research. (by Kaleidophon)
PyForecastTools
Forecast Verification/Validation Tools in Python (by drsteve)
| awesome-experimental-standards-deep-learning | PyForecastTools | |
|---|---|---|
| 1 | 1 | |
| 25 | 30 | |
| - | - | |
| 2.0 | 3.7 | |
| about 1 year ago | 12 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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.
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.
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[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 :-)
PyForecastTools
Posts with mentions or reviews of PyForecastTools.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-03-11.
-
[P] Introducing confidenceinterval, the long missing python library for computing confidence intervals
Nice to see a python implementation of deLong's method - I've had to use pROC (in R) for that in the past. For binary event analysis (among other things) there's also https://github.com/drsteve/PyForecastTools, which also has bootstrapped confidence intervals, or analytic CI using Wald or Agresti-Coull. The terminology is from weather literature, but it covers a lot of the same ground.
What are some alternatives?
When comparing awesome-experimental-standards-deep-learning and PyForecastTools you can also consider the following projects:
yacs - YACS -- Yet Another Configuration System
pillow - Python Imaging Library (Fork)
awesome-deep-learning-music - List of articles related to deep learning applied to music
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
baybe - Bayesian Optimization and Design of Experiments
awesome-experimental-standards-deep-learning vs yacs
PyForecastTools vs pillow
awesome-experimental-standards-deep-learning vs awesome-deep-learning-music
awesome-experimental-standards-deep-learning vs awesome-recruitment
awesome-experimental-standards-deep-learning vs LiDAR-Guide
awesome-experimental-standards-deep-learning vs orion
awesome-experimental-standards-deep-learning vs software-papers
awesome-experimental-standards-deep-learning vs baybe