nni
wtte-rnn
Our great sponsors
nni | wtte-rnn | |
---|---|---|
5 | 3 | |
13,726 | 756 | |
0.9% | - | |
6.7 | 0.0 | |
about 2 months ago | over 3 years ago | |
Python | Python | |
MIT License | 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.
nni
- Filter Pruning for PyTorch
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Automated Machine Learning (AutoML) - 9 Different Ways with Microsoft AI
For a complete tutorial, navigate to this Jupyter Notebook: https://github.com/microsoft/nni/blob/master/examples/notebooks/tabular_data_classification_in_AML.ipynb
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[D] Efficient ways of choosing number of layers/neurons in a neural network
optuna, hyperopt, nni, plenty of less-known tools too.
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Top 10 Developer Trends, Sun Oct 18 2020
microsoft / nni
wtte-rnn
- Pointers to reduce false negatives while not sacrificing accuracy in deep learning
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[R] apd-crs: Cure Rate Survival Analysis in Python
The typical reason you would go with a weibull function is if you want to be able to relax proportional hazard like in this work: https://github.com/ragulpr/wtte-rnn
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Predicting Hard Drive Failure with Machine Learning
You should check out this time-to-event neural network [1].
[1] https://github.com/ragulpr/wtte-rnn
What are some alternatives?
optuna - A hyperparameter optimization framework
easyesn - Python library for Reservoir Computing using Echo State Networks
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
GLOM-TensorFlow - An attempt at the implementation of GLOM, Geoffrey Hinton's paper for emergent part-whole hierarchies from data
autogluon - AutoGluon: Fast and Accurate ML in 3 Lines of Code
neptune-client - 📘 The MLOps stack component for experiment tracking
AutoML - This is a collection of our NAS and Vision Transformer work. [Moved to: https://github.com/microsoft/Cream]
providence
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
archai - Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
automlbenchmark - OpenML AutoML Benchmarking Framework