Deep learning 101

Wednesday, February 6
2:50 PM - 3:30 PM
Room 111/112

In this talk, we will give an overview of the deep learning space starting with a brief history. We will distinguish between deep learning hype vs practical real-world applications, cover how deep learning differs from other machine learning algorithms, go over sample neural net architectures, and provide a step-by-step guide on how to get started.

Specifically, we will cover what type of training data is required and how to prepare it with Apache Spark, followed by how to choose a correct neural net architecture, train, and deploy a deep learning model with TensorFlow on Apache Hadoop 3.1.
Finally, we will wrap-up with deep learning challenges and shortcomings, and offer short- and long-term recommendations to successfully train and deploy deep learning models within your organization to maximize return on investment.


Robert Hryniewicz
Technical Evangelist
Robert Hryniewicz has over 10 years working on various projects related to Artificial Intelligence, Enterprise Software, IoT, Robotics, Blockchain and more. Currently, he’s a Data Scientist and Evangelist at Hortonworks. Previously, Robert was a CTO at a Singularity Labs startup, Sr. Architect at Cisco, NASA et al. He’s a frequent speaker at DataWorks / Hadoop Summits.