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 a complementary Domino...
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 overview of reinforcement...
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. The excerpt and...
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...
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 Learning with Python...
A Practitioner’s Guide to Deep Learning with Ludwig
Joshua Poduska provides a distilled overview of Ludwig including when to use Ludwig’s command-line syntax and when to use its Python API. Introduction New tools are...
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,...
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)...
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 at a recent...
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. The Mueller Report The Mueller Report, officially known as the Report on...
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?...