pytorch-widedeep
saint
| pytorch-widedeep | saint | |
|---|---|---|
| 7 | 1 | |
| 1,238 | 366 | |
| - | - | |
| 8.5 | 1.8 | |
| 6 days ago | over 2 years ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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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.
pytorch-widedeep
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why can't I import pytorch-widedeep ?
Ask the dev https://github.com/jrzaurin/pytorch-widedeep/issues
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[P] pytorch-widedeep model alert: TabPerceiver and TabFastFormer are now available in the library
New DL models for Tabular Data and functionalities added to the pytorch-widedeep library
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[P] pytorch-widedeep model alert: SAINT and the FT-Transformer are now available in the library
🚨MODEL ALERT! 🚨New DL models for Tabular Data added to the pytorch-widedeep library . SAINT by Gowthami Somepalli and collaborators (paper: https://arxiv.org/abs/2106.01342) and the FT-Transformer which was already used at the SAINT paper but officially introduced by Yury Gorishniy and collaborators (paper: https://arxiv.org/abs/2106.11959). More functionalities coming soon to the [library](https://github.com/jrzaurin/pytorch-widedeep)
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How to do K-Fold Cross Validation for hyperparameter tuning on pytorch-widedeep ?
Now in pytorch-widedeep (https://github.com/jrzaurin/pytorch-widedeep) the recommended path for training a single model is:
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[P] pytorch-widedeep v1.0: deep learning for tabular data that you can combine with images and text
Main repo
- Pytorch-widedeep v1.0: deep learning for tabular data
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[P] pytorch-widedeep, deep learning for tabular data: Deep Learning vs LightGBM
A thorough comparison between Deep Learning algorithms for tabular data (using pytorch-widedeep ) and LightGBM for classification and regression problems.
saint
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[D] Tabular Data: Deep Learning is Not All You Need
SAINT paper emerges new, claiming that they have SOTA results than benchmarks, however, after only 2-3 weeks, people say that with fine tuning(not even feature engineering) they can have better results than proposed paper https://github.com/somepago/saint/issues/1 , on exactly same problems on paper.
What are some alternatives?
tabnet - PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf
trax - Trax — Deep Learning with Clear Code and Speed
alibi-detect - Algorithms for outlier, adversarial and drift detection
mmocr - OpenMMLab Text Detection, Recognition and Understanding Toolbox
rtdl - Research on Tabular Deep Learning (Python package & papers) [Moved to: https://github.com/Yura52/rtdl]
torchio - Medical imaging toolkit for deep learning
scrambpy - Scramb.py is a region based JPEG Image Scrambler and Descrambler written in Python for End-to-End-Encrypted (E2EE) Image distribution through unaware channels.
autogluon - Fast and Accurate ML in 3 Lines of Code
LAVIS - LAVIS - A One-stop Library for Language-Vision Intelligence
merged_depth - Monocular Depth Estimation - Weighted-average prediction from multiple pre-trained depth estimation models
CapDec - CapDec: SOTA Zero Shot Image Captioning Using CLIP and GPT2, EMNLP 2022 (findings)