There are many resources available for learning how to leverage Deep Learning to process imagery. However, very few resources exist to demonstrate how to process data from other sensors such as acoustic, seismic, radio, or radar. In this tutorial, we will introduce some basic methods for utilizing a Convolutional Neural Network (CNN) to process Radio Frequency (RF) signals. More specifically, we will look at the classic problem of detecting a weak signal corrupted by noise. We will show you how to leverage the DIGITS application to read in a dataset, train a CNN, adjust hyper-parameters, and then test and evaluate the performance of your model.

Lab created by KickView - Intelligent Processing Applications
Lab Details
Tokens Required: 30 Tokens
Levels: Beginner
Duration: 01 h:30 m
Access Time: 01 h:55 m
Setup Time: 00 h:07 m
Tags: Deep Learning, self-paced, Machine Learning, DIGITS
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