In multitask learning, we aim to improve performance on multiple prediction tasks by solving them simultaneously using models that are related. Neural networks can especially benefit from multitask training in ways that simpler (linear) models cannot. Although multitask neural nets, which were first proposed over 20 years ago, are conceptually simple to design, they can present unexpected challenges. In this lab, we will demonstrate how to build and successfully train multitask neural networks to predict multiple clinical outcomes simultaneously from publicly available digital health data using DeepLearning4J ((DL4J). We will also how to train a similar model using the Keras frontend for TensorFlow and import the resulting model into DL4J for deployment. Prerequisite: Basic knowledge of any programming language. This lab utilizes GPU resources in the cloud, you are required to bring your own laptop.