will-sh3-b33
CodeSearchNet
| will-sh3-b33 | CodeSearchNet | |
|---|---|---|
| 3 | 2 | |
| 3 | 1,904 | |
| - | - | |
| 0.7 | 0.0 | |
| about 1 year ago | about 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 only | MIT License |
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will-sh3-b33
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[D] Are there pytorch notebooks for the Aurelien Geron's book "Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow"?
Also, ML is not all about the actual learning. Data preprocessing is important as well. Hell, KNOWING your data is an important step. As an ML engineer, I wanted a quick way to show my deploying skills so I downloaded an OkCupid dataset and trained 3 shallow and deep networks to tell you whether you'll be alone or not. What I did not realize was that out of 59k records, 56k were single! I fucked up royally but not knowing my data. I also made some mistakes in preprocessing it. If ya wanna see it, go here.
- Will Sh3 B33 is on Github: Predicting whether you'll find love based on OkCupid Data
- My OkCupid project is nearly done!
CodeSearchNet
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Fine tuning
The CodeSearchNet challenge provides a dataset of code documentation comments, along with pre-trained models and fine-tuning scripts. You can find the challenge and resources at https://github.com/github/CodeSearchNet.
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Speedtyper.dev: Type racing for programmers
https://github.com/github/CodeSearchNet#downloading-data-from-s3
What are some alternatives?
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pytorch-GAT - My implementation of the original GAT paper (VeliÄkoviÄ et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
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