uncertainty-baselines
tablespoon
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| uncertainty-baselines | tablespoon | |
|---|---|---|
| 3 | 9 | |
| 1,360 | 39 | |
| 1.7% | - | |
| 5.9 | 5.3 | |
| 19 days ago | 6 months ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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.
uncertainty-baselines
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Google AI Introduces ‘Uncertainty Baselines Library’ For Uncertainty and Robustness in Deep Learning
Code for https://arxiv.org/abs/2106.04015 found: https://github.com/google/uncertainty-baselines
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[D] Mixed Precision Training Tips
I know Reddit likes dumping on TensorFlow but it's actually really easy. Set a policy and upcast your final logits to float32.
tablespoon
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Statistical vs. Deep Learning forecasting methods
I use my package https://github.com/alexhallam/tablespoon to generate naive forecasts then evaluate the crps of the naive vs the crps of the alternative method. This “skill score” approach is very good.
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I made a library that makes naive forecasting easy
Source code is here tablespoon
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[P] Time-series Benchmark methods that are Simple and Probabilistic
tablespoon makes generating these naive methods easy while taking advantage of Stan's efficient No U-Turn Sampler - much the same way Facebook Prophet it built on top of Stan.
- [P] tablespoon: Time-series Benchmark methods that are Simple and Probabilistic
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- GitHub - alexhallam/tablespoon: 🥄✨Time-series Benchmark methods that are Simple and Probabilistic
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✨Announcing development of a benchmark forecasting library to be used as alongside AI forecasting methods. ✨
I just started the development of tablespoon. The purpose of this package is to make time-series benchmark forecasts that are simple and probabilistic.
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Time-series Benchmark methods that are Simple and Probabilistic
tablespoon
What are some alternatives?
pytorch-forecasting - Time series forecasting with PyTorch
Bayeslite - BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.
pyod - A Comprehensive and Scalable Python Library for Outlier Detection (Anomaly Detection)
lambdo - Feature engineering and machine learning: together at last!
Numbers-Prophecy - An experiment to demonstrate the biases and predictability of our world.
probability - Probabilistic reasoning and statistical analysis in TensorFlow
Kats - Kats, a kit to analyze time series data, a lightweight, easy-to-use, generalizable, and extendable framework to perform time series analysis, from understanding the key statistics and characteristics, detecting change points and anomalies, to forecasting future trends.
deep_learning_and_the_game_of_go - Code and other material for the book "Deep Learning and the Game of Go"
sktime - A unified framework for machine learning with time series
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
darts - A python library for user-friendly forecasting and anomaly detection on time series.