Deep Learning Deep Learning Meets DSP: OFDM Signal Detection In this blog post, we'll focus specifically on detection of RF signals modulated using Orthogonal Frequency Division Multiplexing (OFDM) using deep learning.
Deep Learning Choosing a Convolutional Neural Network Architecture for Real-Time Object Tracking (Part 2) This is part 2 of 3 in a series about selecting appropriate network architectures for real-time object tracking. In part 1 we compared the inference speed of various existing object detection networks. Now we turn our attention to accuracy metrics. We will examine some commonly used metrics such as mean
Deep Learning Choosing a Convolutional Neural Network Architecture for Real-Time Object Tracking (Part 1) In a previous blog post we talked about how to train a convolutional neural network(CNN) for object detection in images. Object detection combines classification and localization. One use for object detection is in the problem of object tracking in video data. Advances in deep learning have opened doors to
DGX Getting Python 3 running on a DGX We recieved a DGX station a few weeks ago and it is a powerhouse. We will not dive into the specs of the machine since it was covered in a previous post, where we summarized the time saved by using a DGX for various training tasks. In this post we
DGX The DGX has arrived! If you've ever had the opportunity to train a neural network, you've probably realized that it's a major time saver to start with weights from a pre-trained model. Training a model from scratch can take hours, days, or even sometimes weeks depending on the complexity of the model, the amount
