Tag: Machine Learning

MNIST Expanded: 50,000 New Samples Added

This post provides a distilled overview regarding the rediscovery of 50,000 samples within the MNIST dataset.  MNIST: The Potential Danger of Overfitting Recently, Chhavi Yadav (NYU)...

Machine Learning Product Management: Lessons Learned

This Domino Data Science Field Note covers Pete Skomoroch’s recent Strata London talk. It focuses on his ML product management insights and lessons learned. If you...

Themes and Conferences per Pacoid, Episode 9

Paco Nathan's latest article features several emerging threads adjacent to model interpretability. Introduction Welcome back to our monthly burst of themes and conferences. Several technology conferences...

Addressing Irreproducibility in the Wild

This Domino Data Science Field Note provides highlights and excerpted slides from Chloe Mawer’s "The Ingredients of a Reproducible Machine Learning Model" talk at a recent...

Can Data Science Help Us Make Sense of the Mueller Report?

This blog post provides insights on how to apply Natural Language Processing (NLP) techniques. A complementary Domino project is available. The Mueller Report The Mueller Report,...

Comparing the Functionality of Open Source Natural Language Processing Libraries

In this guest post, Maziyar Panahi and David Talby provide a cheat sheet for choosing open source NLP libraries. What do natural language processing libraries do?...

Themes and Conferences per Pacoid, Episode 8

Paco Nathan's latest column dives into data governance. Introduction Welcome back to our monthly burst of themes and conferences. This month’s article features updates from one...

Highlights from the Maryland Data Science Conference: Deep Learning on Imagery and Text

Niels Kasch, cofounder of Miner & Kasch, an AI and Data Science consulting firm, provides insight from a deep learning session that occurred at the Maryland...

Themes and Conferences per Pacoid, Episode 7

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Introduction Welcome back to our monthly...

On Collaboration Between Data Science, Product, and Engineering Teams

Eugene Mandel, Head of Product at Superconductive Health, recently dropped by Domino HQ to candidly discuss cross-team collaboration within data science. Mandel’s previous leadership roles within...

Machine Learning Projects: Challenges and Best Practices

Lukas Biewald is the founder of Weights & Biases. He was previously the founder of Figure Eight (formerly CrowdFlower). This blog post provides insights into why...

Model Interpretability with TCAV (Testing with Concept Activation Vectors)

This Domino Data Science Field Note provides very distilled insights and excerpts from Been Kim’s recent MLConf 2018 talk and research about Testing with Concept Activation Vectors...

SHAP and LIME Python Libraries: Part 2 – Using SHAP and LIME

This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers prepare...

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