Element 84, Inc. - Machine Learning

Machine Learning

Data Science
Niki LaGrone
Is This a Crosswalk?: 5 Takeaways from Training a Data Labeling Team
We trained remote computer vision workers to provide data labeling for machine learning projects. Here’s what we learned.

Machine Learning
Adeel Hassan
Transfer Learning from RGB to Multi-band Imagery
We used transfer learning to teach a model to take advantage of multi-band imagery without discarding the original RGB pre-training. This resulted in significant performance improvement.
Software Engineering
Justin Walgran
Using the Dedupe Machine Learning Library for Cleaning and Matching Data
To deal with issues of apparel facility list data quality and scale quickly and efficiently we need a machine learning tool that can capture the knowledge of domain experts, find commonalities in jumbled text, and confidently compare large lists without the need to compare each individual entry.
Software Engineering
Lewis Fishgold
Using Noisy Labels to Train Deep Learning Models on Satellite Imagery
How do noisy labels affect the accuracy of a deep learning model? We added different amounts of noise to the SpaceNet Vegas buildings dataset and trained some models to find out.
Software Engineering
Joe Morrison
An Introduction to Machine Learning Accuracy Metrics
How accurate of a machine learning model can you make? It depends on how we decide to define “accurate”.