Have you seen the Silicon Valley episode where they trained a neural network on a mobile phone to recognize whether an object is a Hotdog or not a Hotdog? We're going to build that app, and more, in just a few hours - we will do everything from training a neural network to having an app that can take photos, send it to a deployed server to test it against your trained model (model inferencing). All this will be done with end-to-end TLS security. This workshop is aimed at data scientists and data engineers who are eager to learn about deep learning with Big Data. We will write, train, and deploy a convolutional neural network in Tensorflow/Keras on the Hops platform as a Jupyter Notebook.  We will show you how you can scale out training to reduce training time. We will also show you how to embed Deep Neural Networks (DNNs) in production pipelines for both training and inference using Apache Spark. We will base our tutorial on exercises given to students in Sweden’s first graduate course on Deep Learning – ID2223 at KTH. We will provide both a virtual machine instance and access to the same cluster used in our course, at www.hops.site.

All code and datasets are 100% open source. Code is available from Github at https://github.com/hopshadoop, while datasets are available from Hops Hadoop at www.hops.site. All you will need is your laptop and an Internet connection.