We all know what they say – the bigger the data, the better. But when the data gets really big, how do you use it? This talk will cover three of the most popular deep learning frameworks: TensorFlow, Keras, and Deep Learning Pipelines, and when, where, and how to use them.
We’ll also discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data), as well as help you answer questions such as:
– As a developer how do I pick the right deep learning framework for me?
– Do I want to develop my own model or should I employ an existing one
– How do I strike a trade-off between productivity and control through low-level APIs?
In this session, we will show you how easy it is to build an image classifier with Tensorflow, Keras, and Deep Learning Pipelines in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you, and perhaps with a better sense for how to fool an image classifier!
Session hashtag: #DL4SAIS
Jules S. Damji is an Apache Spark Community and Developer Advocate at Databricks. He is a hands-on developer with over 15 years of experience and has worked at leading companies, such as Sun Microsystems, Netscape, @Home, LoudCloud/Opsware, VeriSign, ProQuest, and Hortonworks, building large-scale distributed systems. He holds a B.Sc and M.Sc in Computer Science and MA in Political Advocacy and Communication from Oregon State University, Cal State, and Johns Hopkins University respectively.
Brooke Wenig is a consultant for Databricks and a teaching associate at UCLA, where she has taught graduate machine learning, senior software engineering, and introductory programming courses. Previously, Brooke worked at Splunk and Under Armour as a KPCB fellow. She holds an MS in computer science with highest honors from UCLA with a focus on distributed machine learning. Brooke speaks Mandarin Chinese fluently and enjoys cycling.