FFU_VSE_Masters_Thesis_ML_Credit_Risk_Modelling
ludwig
| FFU_VSE_Masters_Thesis_ML_Credit_Risk_Modelling | ludwig | |
|---|---|---|
| 2 | 3 | |
| 1 | 11,496 | |
| - | 0.5% | |
| 8.5 | 2.7 | |
| almost 2 years ago | 17 days ago | |
| Jupyter Notebook | Python | |
| - | Apache License 2.0 |
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FFU_VSE_Masters_Thesis_ML_Credit_Risk_Modelling
- [TASK] Python- simple classification algorythms
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[Hiring] Python Short task - simple classifier algorythms
Preferebly get the scripts from kaggle/github etc. one possible source to rewrite the scripts is here: https://github.com/petr-ngn/FFU_VSE_Masters_Thesis_ML_Credit_Risk_Modelling
ludwig
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Show HN: Toolkit for LLM Fine-Tuning, Ablating and Testing
This is a great project, little bit similar to https://github.com/ludwig-ai/ludwig, but it includes testing capabilities and ablation.
questions regarding the LLM testing aspect: How extensive is the test coverage for LLM use cases, and what is the current state of this project area? Do you offer any guarantees, or is it considered an open-ended problem?
Would love to see more progress toward this area!
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Python projects with best practices on Github?
Two random examples I found from 30 seconds of googling: Here’s Netflix using it in their crisis management tool, and here’s Uber using it in their deep learning framework.
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Most Frequent 600 Coding Questions on LeetCode
They list themselves all over the internet as an "open source contributor" to Uber, which as far I can tell is based entirely on... reporting that there was an issue with a favicon. To me, it seems like they'll be cheating anybody who employs them based on this, ahem, "experience". And that feels like the tip of the iceberg.
What are some alternatives?
surv_ai - A framework to empower forecasting using Large Language Models (LLMs) [GET https://api.github.com/repos/DanielBalsam/surv_ai: 404 - Not Found // See: https://docs.github.com/rest/repos/repos#get-a-repository]
ai-deadlines - :alarm_clock: AI conference deadline countdowns
market_risk_gan_keras - Using Bidirectional Generative Adversarial Networks to estimate Value-at-Risk for Market Risk Management using TensorFlow. [Moved to: https://github.com/hamaadshah/market_risk_gan_tensorflow]
nlp-recipes - Natural Language Processing Best Practices & Examples
practical-mlops-book - [Book-2021] Practical MLOps O'Reilly Book
Robo-Semantic-Segmentation - Just a simple semantic segmentation library that I developed to speed up the image segmentation pipeline