VevestaX
recommenders
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| VevestaX | recommenders | |
|---|---|---|
| 10 | 6 | |
| 27 | 17,942 | |
| - | 2.0% | |
| 0.0 | 9.4 | |
| over 1 year ago | 13 days ago | |
| Jupyter Notebook | Python | |
| 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.
VevestaX
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đź“ťEverything you need to know about Distributed training and its often untold nuances
100 early birds who login into www.vevesta.com will get a free lifetime subscription.
- [D] Open Source library to do automatic EDA + experiment tracking in a spreadsheet
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[D] ZIP models as a means to handle regression on data with excess of zeros
Sharing an article on how to handle regression for data which has lots and lots of zeros. VevestaX/ZIP_tutorial.md at main · Vevesta/VevestaX · GitHub
- Zero Inflated Poisson Regression Model – How to model data with lot of zeroes?
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MLflow VS VevestaX - a user suggested alternative
2 projects | 12 May 2022
- Show HN: Discover VevestaX – Track ML features, experiments and EDA in an Excel
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[D] Impactful Computer Vision Research - Nerf (Neural Radiance Fields)
On side note, we have developed a knowledge repository for Machine Learning Projects with note taking ability. We are looking for beta testers. Check us out on www.vevesta.com or mail us on [[email protected]](mailto:[email protected]). Eager to hear your views on the same.
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VevestaX - An awesome and simple tool to track ML experiments in an excel file
You can check out the source code at our GitHub page: https://github.com/Vevesta/VevestaX.
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VevestaX - Library to track ML experiments and data into an excel file
Gitlink: https://github.com/Vevesta/VevestaX
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?
bodywork-pipeline-with-aporia-monitoring - Integrating Aporia ML model monitoring into a Bodywork serving pipeline.
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
MLOps - End to End toy example of MLOps
azure-devops-python-api - Azure DevOps Python API
vertex-ai-samples - Sample code and notebooks for Vertex AI, the end-to-end machine learning platform on Google Cloud
python-minecraft-clone - Source code for each episode of my Minecraft clone in Python YouTube tutorial series.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
mlflow-deployments - Source code for the post Effortless deployments with MLFlow, showcasing how logging models using MLFLow can provide you want to easily deploy them in production later.
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]
mlflow-easyauth - Deploy MLflow with HTTP basic authentication using Docker
Google-rank-tracker - SEO: Python script + shell script and cronjob to check ranks on a daily basis