youtube-react
Spearmint
| youtube-react | Spearmint | |
|---|---|---|
| 1 | 2 | |
| 562 | 1,529 | |
| - | 0.0% | |
| 1.9 | 0.0 | |
| 12 months ago | over 4 years ago | |
| JavaScript | Python | |
| - | GNU General Public License v3.0 or later |
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youtube-react
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The Truth On The Recent Cyberbullying (Details In Text)
So DevSpace ('your' version), Videofy ('yours'), ImgCleaner ('yours') weren't projects on your resume?
Spearmint
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Why do tree-based models still outperform deep learning on tabular data?
It occurs to me that a system, trained on peer-reviewed applied-machine-learning literature and Kaggle winners, that generates candidates for structured feature-engineering specifications, based on plaintext descriptions of columns' real-world meaning, should be considered a requisite part of the "meta" here.
Ah, and then you could iterate within the resulting feature-engineering-suggestion space as a hyper-parameter between experiments, which could be optimized with e.g. https://github.com/HIPS/Spearmint . The papers write themselves!
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[D] What kind of Hyperparameter Optimisation do you use?
This was some time ago but I had some promising results with Bayesian optimization using a Gaussian Process prior. The method was developed by the guys who wrote Spearmint. That library doesn't support parallelization but I implemented the same technique in Scala without too much difficulty.
What are some alternatives?
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devconnector_2.0 - Social network for developers, built on the MERN stack
spaceopt - Hyperparameter optimization via gradient boosting regression
enzyme-to-json - Snapshot test your Enzyme wrappers
decision-forests - A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.