Subject archive for "practical-techniques," page 8

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 Data Science Conference.

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 about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev.

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 has already written a complementary blog post on using spaCy to process text data for Domino. Karau is a Developer Advocate at Google as well as a co-author on High Performance Spark and Learning Spark. She also has a repository of her talks, code reviews, and code sessions on Twitch and Youtube.

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 as a co-author of "High Performance Spark" and "Learning Spark". She has a repository of her talks, code reviews and code sessions on Twitch and YouTube. She is also working on Distributed Computing 4 Kids.

In this guest blog post, Derrick Higgins covers item response theory (IRT) and how data scientists can apply it within a project. As a complement to the guest blog post, there is also a demo within Domino.

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. If interested in additional insight from Poduska, he will also be presenting "Managing Data Science in the Enterprise" at Strata New York 2018.