Tag: Data Science

The importance of structure, coding style, and refactoring in notebooks

Notebooks are increasingly crucial in the data scientist's toolbox. Although considered relatively new, their history traces back to systems like Mathematica and MATLAB. This form of...

Domino Paves the Way for the Future of Enterprise Data Science with Latest Release

Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. We’re introducing groundbreaking new features –...

Evaluating Ray: Distributed Python for Massive Scalability

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you are interested...

Evaluating Generative Adversarial Networks (GANs)

This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. If you are interested in a tutorial...

Announcement: Domino is fully Kubernetes native

Last week we announced that Domino is now fully Kubernetes native. This is great news for data science teams and IT organizations building modern DS platforms,...

Themes and Conferences per Pacoid, Episode 13

Paco Nathan's latest article covers data practices from the National Oceanic and Atmospheric Administration (NOAA) Environment Data Management (EDM) workshop as well as updates from the...

Deep Learning Illustrated: Building Natural Language Processing Models

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt "Natural Language Processing" from the book, Deep Learning Illustrated by Krohn, Beyleveld, and Bassens. The...

Data Ethics: Contesting Truth and Rearranging Power

This Domino Data Science Field Note covers Chris Wiggins's recent data ethics seminar at Berkeley. The article focuses on 1) proposed frameworks for defining and designing...

Data Science, Past & Future

Paco Nathan presented, "Data Science, Past & Future", at Rev. This blog post provides a concise session summary, a video, and a written transcript. Session Summary...

Data Science at The New York Times

Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find...

Themes and Conferences per Pacoid, Episode 11

Paco Nathan's latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Introduction Welcome back to our monthly burst of themespotting and conference summaries. BTW,...

Announcing Trial and Domino 3.5: Control Center for Data Science Leaders

Even the most sophisticated data science organizations struggle to keep track of their data science projects. Data science leaders want to know, at any given moment,...

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)...

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