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2017 GTC San Jose

L7136 - Deep Multitask Prediction with Digital Health Data

Session Speakers
Session Description

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.


Additional Session Information
Beginner
Instructor-Led Lab
AI in Healthcare Summit Deep Learning and AI Programming Languages Video and Image Processing
Healthcare & Life Sciences
2 hours
Session Schedule