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....
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
Manual Feature Engineering
Many thanks to AWP Pearson for the permission to excerpt "Manual Feature Engineering: Manipulating Data for Fun and Profit" from the book, Machine...
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...
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 &...
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...
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...
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...