Subject archive for "model-development," page 8
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Stakeholder-Driven Data Science at Warby Parker
Max Shron, the head of data science at Warby Parker, delivered a presentation on stakeholder-driven data science at a Data Science Popup. This blog post provides a session summary, a video of the entire session, and a video transcript of the presentation. If you are interested in attending a future Data Science Popup, the next event is November 14th in Chicago.
By Domino32 min read
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Humans in the Loop
Humans and Machines: SciFi or Already Commonplace?
By Paco Nathan9 min read
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Recommender Systems through Collaborative Filtering
This is a technical deep dive into the collaborative filtering algorithm and how to use it in practice.
By Manojit Nandi15 min read
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Sampling Based Methods for Class Imbalance in Datasets
Imagine you are a medical professional who is training a classifier to detect whether an individual has an extremely rare disease. You train your classifier, and it yields 99.9% accuracy on your test set. You're overcome with joy by these results, but when you check the labels outputted by the classifier, you see it always outputted "No Disease," regardless of the patient data. What's going on?!
By Manojit Nandi11 min read
![](https://cdn.sanity.io/images/kuana2sp/production-main/3b412dd40c5505314c147dc0d71b3caa25f9fd20-2000x1333.jpg?w=650&fit=max&auto=format)
Model-Based Machine Learning and Probabilistic Programming in RStan
In this recorded webcast, Daniel Emaasit introduces model-based machine learning and related concepts, practices and tools such as Bayes' Theorem, probabilistic programming, and RStan.
By Daniel Chalef1 min read
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Data Science Platform: What is it? Why is it Important?
As more companies recognize the need for a [data science platform], more vendors are claiming they have one. Increasingly, we see companies describing their product as a “data science platform” without describing the features that make platforms so valuable. So we wanted to share our vision for the core capabilities a platform should have in order for it to be valuable to data science teams.
By Nick Elprin7 min read
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