Tamber Ruby
Recommendation engines for developers, easy as π. https://tamber.com
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Applied Ml | 24,828 | 8 months ago | 3 | mit | ||||||
| 📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production. | ||||||||||
| Se3 Transformer Pytorch | 205 |  | 1 | 2 years ago | 78 | November 25, 2022 | 5 | mit | Python | |
| Implementation of SE3-Transformers for Equivariant Self-Attention, in Pytorch. This specific repository is geared towards integration with eventual Alphafold2 replication. | ||||||||||
| And | 105 | 5 years ago | 2 | mit | Python | |||||
| Official Pytorch Implementation for ICML'19 paper: Unsupervised Deep Learning by Neighbourhood Discovery | ||||||||||
| Dads | 82 | 4 years ago | 1 | apache-2.0 | Python | |||||
| Code for 'Dynamics-Aware Unsupervised Discovery of Skills' (DADS). Enables skill discovery without supervision, which can be combined with model-based control. | ||||||||||
| Stnn | 67 | 5 years ago | 2 | bsd-2-clause | Jupyter Notebook | |||||
| Code for the paper "Spatio-Temporal Neural Networks for Space-Time Series Modeling and Relations Discovery" | ||||||||||
| Deep Learning For Human Part Discovery In Images | 63 | 7 years ago | 5 | Python | ||||||
| Implementation with Chainer | ||||||||||
| Tamber Ruby | 25 | 4 years ago | 1 | mit | Ruby | |||||
| Recommendation engines for developers, easy as π. https://tamber.com | ||||||||||
| Ddt | 24 | 6 years ago | Python | |||||||
| Third-party PyTorch implementation of DDT(Unsupervised object discovery and co-localization by deep descriptor transformation) | ||||||||||
| Mlchempapers | 11 | 7 years ago | ||||||||
| A sorted list of ML (mostly deep learning) papers relating to chemistry, biology, and drug discovery. | ||||||||||
| Ai_in_drug_discovery_progress | 7 | 5 years ago | apache-2.0 | |||||||
| An up-to-date list of papers and code relating to progress in the burgeoning field of AI-Drug Discovery | ||||||||||
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