rtdl-revisiting-models
threat-research-and-intelligence
rtdl-revisiting-models | threat-research-and-intelligence | |
---|---|---|
1 | 1 | |
156 | 89 | |
4.5% | - | |
6.6 | 5.9 | |
6 days ago | 7 months ago | |
Python | Jupyter Notebook | |
Apache License 2.0 | Apache License 2.0 |
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.
rtdl-revisiting-models
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[R] New paper on Tabular DL: "On Embeddings for Numerical Features in Tabular Deep Learning"
JFYI: recently, we have split our codebase into separate projects: - https://github.com/Yura52/rtdl - https://github.com/Yura52/tabular-dl-revisiting-models - (the new one) https://github.com/Yura52/tabular-dl-num-embeddings
threat-research-and-intelligence
What are some alternatives?
rtdl-num-embeddings - (NeurIPS 2022) On Embeddings for Numerical Features in Tabular Deep Learning
AMAYARA-Lab - The アマヤラ Lab project provides a ready-to-use Jupyter Lab environment to help out with Android malware analysis using YARA rules.
rtdl - Research on Tabular Deep Learning (Python package & papers) [Moved to: https://github.com/Yura52/rtdl]
datasets - 🎁 5,400,000+ Unsplash images made available for research and machine learning
Watermark-Removal-Pytorch - 🔥 CNN for Watermark Removal using Deep Image Prior with Pytorch 🔥.
hyperlearn - 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.
lava-dl - Deep Learning library for Lava
cobaltstrike-beacon-data - Open Dataset of Cobalt Strike Beacon metadata (2018-2022)
conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
mnist1d - A 1D analogue of the MNIST dataset for measuring spatial biases and answering Science of Deep Learning questions.