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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Reviews 54

  1. Jeremy Slater
    can't get it to run
    Jeremy Slater Reviewed 2 days ago
  2. 森山 朋美

    森山 朋美 Reviewed 10 days ago
  3. Matt Wilkinson

    Matt Wilkinson Reviewed 10 days ago
  4. John Caskey

    John Caskey Reviewed 12 days ago
  5. Codie Petersen
    Simple Straight Forward, but informative.
    Codie Petersen Reviewed 13 days ago