Speaker "Josh Patterson" Details Back



Enterprise Deep Learning with DL4J


As the data world undergoes its cambrian explosion phase our data tools need to become more advanced to keep pace. Deep Learning has emerged as a key tool in the non-linear arms race of machine learning. Applications in text, sensor processing (IoT), image processing, and audio processing have all emerged as prime deep learning applications. In this session we will take a look at a practical review of what is deep learning and how DL4J’s architecture allows the Fortune 500 to easily build deep learning models with large amounts of data on Hadoop and Spark. We’ll also look at practical workflows for building next-generation applications with the DL4J tool suite.


Josh Patterson currently runs a consultancy in the big data machine learning space. Previously Josh worked as a Principal Solutions Architect at Cloudera and as a machine learning / distributed systems engineer at the Tennessee Valley Authority where he brought Hadoop into the smart grid with the openPDC project. Josh has a Masters in Computer Science from the University of Tennessee at Chattanooga where he did published research on mesh networks (tinyOS) and social insect optimization algorithms. Josh has over 18 years in software development and is very active in the open source space with projects such as DL4J, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.