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,...
Machine Learning in Production: Software Architecture
Special thanks to Addison-Wesley Professional for permission to excerpt the following "Software Architecture" chapter from the book, Machine Learning in Production. This chapter excerpt provides data...
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?...
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 Science: Start Writing...
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 5
In Paco Nathan's latest column, he explores the theme of "learning data science" by diving into education programs, learning materials, educational approaches, as well as perceptions...
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 spaCy into Java. She...
Making PySpark Work with spaCy: Overcoming Serialization Errors
In this guest post, Holden Karau, Apache Spark Committer, provides insights on how to use spaCy to process text data. Karau is a Developer Advocate at Google, as well...
Item Response Theory in R for Survey Analysis
In this guest blog post, Derrick Higgins, of American Family Insurance, covers item response theory (IRT) and how data scientists can apply it within a project. As...
Benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST
In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, writes about benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST....
Learn from the Reproducibility Crisis in Science
Key highlights from Clare Gollnick’s talk, “The limits of inference: what data scientists can learn from the reproducibility crisis in science”, are covered in this Domino...
Feature Engineering: A Framework and Techniques
This Domino Field Note provides highlights and excerpted slides from Amanda Casari’s “Feature Engineering for Machine Learning” talk at QCon Sao Paulo. Casari is the Principal...