opnazure
recommenders
Our great sponsors
| opnazure | recommenders | |
|---|---|---|
| 2 | 6 | |
| 144 | 17,980 | |
| - | 2.2% | |
| 7.2 | 9.5 | |
| 5 months ago | 6 days ago | |
| Bicep | Python | |
| MIT License | 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.
opnazure
- S2S VPN for Test Lab
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Free (Community) edition of pfSense on Azure - weird pricing?
This template could be a good option if you wanna use OPNsense instead of pfSense. Perfect for production environment, https://github.com/dmauser/opnazure
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?
Enterprise-Scale - The Azure Landing Zones (Enterprise-Scale) architecture provides prescriptive guidance coupled with Azure best practices, and it follows design principles across the critical design areas for organizations to define their Azure architecture
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
azure-quickstart-templates - Azure Quickstart Templates
azure-devops-python-api - Azure DevOps Python API
ALZ-Bicep - This repository contains the Azure Landing Zones (ALZ) Bicep modules that help deliver and deploy the Azure Landing Zone conceptual architecture in a modular approach. https://aka.ms/alz/docs
python-minecraft-clone - Source code for each episode of my Minecraft clone in Python YouTube tutorial series.
ResourceModules - This repository includes a CI platform for and collection of mature and curated Bicep modules. The platform supports both ARM and Bicep and can be leveraged using GitHub actions as well as Azure DevOps pipelines.
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
aks-baseline - This is the Azure Kubernetes Service (AKS) Baseline Cluster reference implementation as produced by the Microsoft Azure Architecture Center.
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]
Google-rank-tracker - SEO: Python script + shell script and cronjob to check ranks on a daily basis
horapy - 🐍 Python bidding for the Hora Approximate Nearest Neighbor Search Algorithm library