Alan Feder

Alan Feder is a Principal Data Scientist at Invesco, where he uses as much R as possible to solve problems and build products throughout the company. Previously, he worked as a data scientist at AIG and an actuary at Swiss Re. He studied statistics and mathematics at Columbia University. He is unreasonably excited to spread the word about categorical embeddings. Alan lives in New York City with his wife, Ashira, and two children, Matan and Sarit.


Categorical Embeddings: New Ways to Simplify Complex Data

rstudio::global 2021

Categorical Embeddings: New Ways to Simplify Complex Data

January 21, 2021

Categorical embeddings are a relative new method, utilizing methods popularized in Natural Language Processing that help models solve this problem and can help you understand more about the categories themselves.