Strata Data Conference is coming to New York September 25- 28. There is a wealth of content for people interested in data science and machine learning. The conference offers 22 tutorials on Tuesday, September 26. Ten of these feature data science or machine learning, covering topics that include Spark ML, TensorFlow, DL4J, Keras, R, and other tools. Among the highlights:
— In the morning session, Vartika Singh and Jeffrey Shmain of Cloudera lead hands-on training in machine learning and deep learning on Apache Spark.
— Vartika Singh returns in the afternoon session and partners with Josh Patterson, Dave Kale, and Tom Hanlon of Skymind. Together, they teach you how to use DL4J and Cloudera Data Science Workbench to securely build deep learning models with digital health data.
Keynotes and sessions begin on Wednesday, September 27, and continue through the next day. There are 26 sessions that cover machine learning and data science. Highlights include:
— On Wednesday at 1:15 pm, Clouderans Thomas Dinsmore and Tristan Zajonc team with Lucas Glass of QuintilesIMS. They discuss the importance of collaboration in data science, common barriers and challenges, and technical solutions.
— Also on Wednesday, at 2:55 pm, Holden Karau and Cloudera’s Seth Hendrickson explain how to extend Spark ML with your own tools and algorithms.
— At 5:25 pm, Seth Hendrickson joins Netflix’ DB Tsai to show you how to boost the performance of Spark MLlib
— On Thursday at 1:15 pm, Cloudera’s Steve Totman and Faraz Rasheed from TD Bank introduce you to Griffin, a high-level easy-to-use modeling platform built on Apache Spark.
— Also on Thursday, at 11:20 am, Hilary Mason of Cloudera Fast Forward Labs reviews the current state of interaction between humans and machines through natural language.
— At 1:15 pm, in an Executive Briefing session, Cloudera’s Chief Strategy Officer Mike Olson explains machine learning: why you need it, why it’s hard, and what to do about it.
Also, be sure to visit Cloudera at Booth #225 to learn more about Cloudera for data science and machine learning.