Clustering in R
This article covers clustering including K-means and hierarchical clustering. A complementary Domino project is available. Introduction Clustering is a machine learning technique that...
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...
Understanding Causal Inference
This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows...
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
Natural Language in Python using spaCy: An Introduction
This article provides a brief introduction to natural language using spaCy and related libraries in Python. The complementary Domino project is also available....
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...
Machine Learning
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,...
Make Machine Learning Interpretability More Rigorous
This Domino Data Science Field Note covers a proposed definition of machine learning interpretability, why interpretability matters, and the arguments for considering a...
Practical Techniques
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,...
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...
Leaders at Work
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...
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...
HyperOpt: Bayesian Hyperparameter Optimization
This article covers how to perform hyperparameter optimization using a sequential model-based optimization (SMBO) technique implemented in the HyperOpt Python package. There is...
Deep Reinforcement Learning
This article provides an excerpt "Deep Reinforcement Learning" from the book, Deep Learning Illustrated by Krohn, Beyleveld, and Bassens. The article includes an...
Towards Predictive Accuracy: Tuning Hyperparameters and Pipelines
This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Fenner....
Seeking Reproducibility within Social Science: Search and Discovery
Julia Lane, NYU Professor, Economist and cofounder of the Coleridge Initiative, presented “Where’s the Data: A New Approach to Social Science Search &...