NaiveNASlib.jl
autogluon
| NaiveNASlib.jl | autogluon | |
|---|---|---|
| 1 | 8 | |
| 17 | 7,124 | |
| - | 1.6% | |
| 6.7 | 9.6 | |
| 4 months ago | 8 days ago | |
| Julia | Python | |
| MIT License | Apache License 2.0 |
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NaiveNASlib.jl
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[R] Python ML Library for defining highly granular network architectures?
I made this a while ago to experiment with neural architecture search: https://github.com/DrChainsaw/NaiveNASlib.jl
autogluon
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pip install remyxai - easiest way to create custom vision models
This seems not very convincing. There are other popular frameworks that provide AutoML with existing datasets (eg https://github.com/autogluon/autogluon)
- autogluon: NEW Data - star count:5070.0
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[D] Where is AutoML for NNs?
https://github.com/awslabs/autogluon works well for image/text/tabular data
- k-fold bagging in Autogluon - Tabular
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What will the data science job market be like in 5 years?
Some AutoML is getting pretty good, AutoGluon is very solid for tabular data. That being said you still need to have your data in tabular format and deployment still requires some effort.
What are some alternatives?
BetaML.jl - Beta Machine Learning Toolkit
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
autogluon - AutoGluon: AutoML for Image, Text, Time Series, and Tabular Data [Moved to: https://github.com/autogluon/autogluon]
autokeras - AutoML library for deep learning
NaiveGAflux.jl - Evolve Flux networks from scratch!
auto-sklearn - Automated Machine Learning with scikit-learn
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Lux.jl - Explicitly Parameterized Neural Networks in Julia
imbalanced-regression - [ICML 2021, Long Talk] Delving into Deep Imbalanced Regression
tabnet - PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
automlbenchmark - OpenML AutoML Benchmarking Framework