python-cheatsheet
recommenders
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| python-cheatsheet | recommenders | |
|---|---|---|
| 10 | 6 | |
| 35,404 | 17,980 | |
| - | 2.2% | |
| 9.0 | 9.5 | |
| 8 days ago | 7 days ago | |
| Python | Python | |
| - | 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.
python-cheatsheet
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Top GitHub Resources to Level Up Your Python game
🎇 Repository Link: Python Cheatsheet
- Comprehensive Python “Cheatsheet”
- Comprehensive Python Cheatsheet
- Flash cards
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This Week in Python
python-cheatsheet – Comprehensive Python Cheatsheet
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Java, C, C++ and Python cheat sheets
I like this one on GitHub for Python: https://github.com/gto76/python-cheatsheet
- Comprehensive Python Cheatsheet - It's HUGE
- The biggest python cheatsheet
recommenders
- My kernel dies when I fit my LightFm model from Microsoft Recommenders
- There is framework for everything.
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This Week in Python
recommenders – Best Practices on Recommendation Systems
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Input to SVD, SAR, NMF
I would like to do a benchmarking on the Microsoft models SVD, SAR and NMF (available here: https://github.com/microsoft/recommenders) but with this input data I get a precision and recall close to zero. Any ideas how I can improve this? For SVD and NMF (surprise library) the model wants a rating input that is normally distributed, which it not the case for my binary data where the transactions all have a rating of 1.
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Opinion on choice of model - Recommender System
Then I tried to find some more advanced models and I found this really good list and in there I found the Microsoft one. So it's' where we are now, which a bunch of different models and not a documentation/tutorials out there.
What are some alternatives?
sportsipy - A free sports API written for python
metarank - A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
perflint - Python Linter for performance anti patterns
azure-devops-python-api - Azure DevOps Python API
UltraDict - Sychronized, streaming Python dictionary that uses shared memory as a backend
python-minecraft-clone - Source code for each episode of my Minecraft clone in Python YouTube tutorial series.
gdscript-docs-maker - Create documentation and class references from your Godot GDScript code
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
programming-with-ada - A guide for learning about the Ada Programming Language.
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
py-dynacli - DynaCLI is a cloud-friendly Python library for converting pure Python functions into Linux Shell commands.
Google-rank-tracker - SEO: Python script + shell script and cronjob to check ranks on a daily basis