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,...
Themes and Conferences per Pacoid, Episode 10
Co-chair Paco Nathan provides highlights of Rev 2, a data science leaders summit. Introduction Welcome back to our monthly burst of themespotting and...
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...
Reflections on the Data Science Platform Market
Reflections Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve...
Code
Creating Multi-language Pipelines with Apache Spark or Avoid Having to Rewrite spaCy into Java
In this guest post, Holden Karau, Apache Spark Committer, provides insights on how to create multi-language pipelines with Apache Spark and avoid rewriting...
Data Scientist? Programmer? Are They Mutually Exclusive?
This Domino Data Science Field Note blog post provides highlights of Hadley Wickham’s ACM Chicago talk, “You Can’t Do Data Science in a GUI”....
Machine Learning
On Ingesting Kate Crawford’s “The Trouble with Bias”
Kate Crawford discussed bias at a recent SF-based City Arts and Lectures talk and a recording of the discussion will be broadcast, May...
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...
Practical Techniques
Manipulating Data with dplyr
Special thanks to Addison-Wesley Professional for permission to excerpt the following "Manipulating data with dplyr" chapter from the book, Programming Skills for Data...
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...
Leaders at Work
Collaboration Between Data Science and Data Engineering: True or False?
This blog post includes candid insights about addressing tension points that arise when people collaborate on developing and deploying models. Domino’s Head of...
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...
Product Management for AI
Pete Skomoroch presented “Product Management for AI” at Rev. This post provides a distilled summary, video, and full transcript. Session Summary Pete Skomoroch’s...
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...
Trust in LIME: Yes, No, Maybe So?
In this Domino Data Science Field Note, we briefly discuss an algorithm and framework for generating explanations, LIME (Local Interpretable Model-Agnostic Explanations), that...
Classify all the Things (with Multiple Labels)
Derrick Higgins of American Family Insurance presented a talk, “Classify all the Things (with multiple labels): The most common type of modeling task no...