Advanced Deep Learning with DLI
Advanced Deep Learning with DLI


There are a variety of important applications that need to go beyond detecting individual objects within an image, and that instead need to segment the image into spatial regions of interest. An example of image segmentation involves medical imagery analysis, where it is often important to separate the pixels corresponding to different types of tissue, blood or abnormal cells, so that you can isolate a particular organ. Another example includes self-driving cars, where segmenting an image into distinct areas is needed to understand road scenes. In this lab, you will learn how to train and evaluate an image segmentation network using TensorFlow.


Charles (Charlie) Killam, LP.D. is a Senior Deep Learning Institute Instructor at NVIDIA. Though Charlie works across all verticals, his efforts focus primarily on the application of deep neural networks (DNNs) in the healthcare space. Prior to NVIDIA, Charlie’s experience includes delivering a data analytics bootcamp for Northeastern University, a geospatial Tableau project for Stanford University, and working with MADlib – an open-source, machine learning algorithm library while at Pivotal.

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