| SuperDuperDB/superduperdb |
3,924 |
|
0 |
0 |
about 2 years ago |
5 |
January 13, 2023 |
162 |
apache-2.0 |
Python |
| 🔮 SuperDuperDB: Bring AI to your database! Build, deploy and manage any AI application directly with your existing data infrastructure, without moving your data. Including streaming inference, scalable model training and vector search. |
| danielhanchen/hyperlearn |
1,387 |
|
0 |
0 |
almost 4 years ago |
0 |
|
0 |
apache-2.0 |
Jupyter Notebook |
| 2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old. |
| jrieke/traingenerator |
1,184 |
|
0 |
0 |
almost 4 years ago |
0 |
|
13 |
mit |
Python |
| 🧙 A web app to generate template code for machine learning |
| benedekrozemberczki/CapsGNN |
1,180 |
|
0 |
0 |
about 3 years ago |
0 |
|
3 |
gpl-3.0 |
Python |
| A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019). |
| ryfeus/lambda-packs |
1,100 |
|
0 |
0 |
over 2 years ago |
0 |
|
13 |
mit |
Python |
| Precompiled packages for AWS Lambda |
| charliedream1/ai_quant_trade |
819 |
|
0 |
0 |
about 2 years ago |
0 |
|
2 |
apache-2.0 |
Jupyter Notebook |
| 股票AI操盘手：从学习、模拟到实盘，一站式平台。包含股票知识、策略实例、因子挖掘、传统策略、机器学习、深度学习、强化学习、图网络、高频交易、C++部署和聚宽实例代码等，可以方便学习、模拟及实盘交易 |
| benedekrozemberczki/SimGNN |
540 |
|
0 |
0 |
over 3 years ago |
0 |
|
2 |
gpl-3.0 |
Python |
| A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019). |
| benedekrozemberczki/GraphWaveletNeuralNetwork |
501 |
|
0 |
0 |
about 3 years ago |
0 |
|
0 |
gpl-3.0 |
Python |
| A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019) |
| ploomber/sklearn-evaluation |
429 |
|
2 |
3 |
about 2 years ago |
48 |
December 28, 2020 |
2 |
apache-2.0 |
Python |
| Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis. |
| benedekrozemberczki/AttentionWalk |
299 |
|
0 |
0 |
over 3 years ago |
0 |
|
0 |
gpl-3.0 |
Python |
| A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018). |
