clrs
algo-ds-101
| clrs | algo-ds-101 | |
|---|---|---|
| 4 | 4 | |
| 461 | 151 | |
| 1.3% | - | |
| 7.5 | 9.6 | |
| about 1 month ago | over 3 years ago | |
| Jupyter Notebook | C++ | |
| Apache License 2.0 | MIT License |
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.
clrs
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[P] GITModel: Dynamically generate high-quality hierarchical topic tree representations of GitHub repositories using customizable GNN message passing layers, chatgpt, and topic modeling.
Example: https://github.com/deepmind/clrs
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[D] Is there any research into using neural networks to discover classical algorithms?
Bonus, there is this CLRS benchmark that might be useful for evaluation for your task.
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[R] A Generalist Neural Algorithmic Learner
The baseline code for the CLRS benchmark (which we use in the paper) has been open-sourced for a while now: https://github.com/deepmind/clrs
- GitHub - deepmind/clrs
algo-ds-101
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First 6 pull requests DONE!
Sum of AP in JS for algo_ds_101
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Hacktoberfest! The first PR!
See, the requirement is not so hard. I believe with the basic knowledge of programming, any of us could solve this problem easily. You can have a look at my pull request at my first full request.
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