Tag: Model Development

The Machine Learning Reproducibility Crisis

Pete Warden is the Technical Lead on the TensorFlow Mobile Embedded Team at Google doing Deep Learning. He is formerly the CTO of Jetpac, which was...

Managing Data Science as a Capability

Nick Elprin, CEO at Domino, presented a 3-hour training workshop, “Managing Data Science in the Enterprise”, that provided practical insights and interactive breakouts. The learnings, anecdotes,...

0.05 is an Arbitrary Cut Off: “Turning Fails into Wins”

Grace Tang, Data Scientist at Uber, presented insights, common pitfalls, and “best practices to ensure all experiments are useful” in her Strata Singapore session, “Turning Fails...

Data Science Use Cases

In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next. Don is a cofounder of...

Data Quality Analytics

Scott Murdoch, PhD, Director of Data Science at HealthJoy, presents how data scientists can use distribution and modeling techniques to understand the pitfalls in their data...

Using Bayesian Methods to Clean Up Human Labels

Derrick Higgins, AmFam Data Science & Analytics, discusses how Bayesian methods can be applied to improve the quality of annotated training sets. Session Summary Derrick Higgins,...

Best Practices for Managing Data Science at Scale

We recently published a practical guide for data science management intended to help current and aspiring managers learn from the challenges and successes of industry leaders....

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...

Humans in the Loop

This guest blog post from Paco Nathan dives into how people and machines collaborating together to perform work is real and not science fiction. Paco Nathan is...

Recommender Systems through Collaborative Filtering

This is a technical deep dive of the collaborative filtering algorithm and how to use it in practice. From Amazon recommending products you may be interested...

Imbalanced 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...

Fitting Gaussian Process Models in Python

[mathjax] Written by Chris Fonnesbeck, Assistant Professor of Biostatistics, Vanderbilt University Medical Center. You can view, fork, and play with this project on the Domino data...

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. The field...

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