Load pretrained state of the art model
Create train and eval function to write the training loop
Understand Spectogram Augmentations
In this 2-hour long guided-project course, you will load a pretrained state of the art model CNN and you will train in PyTorch to classify radio signals with input as spectogram images. The data that you will use, consists of spectogram images (spectogram is a representation of audio signals) and there are targets such as ( Squiggle, Noises, Narrowband, etc). Furthermore, you will apply spectogram augmentation for classification task to augment spectogram images. Moreover, you are going to create train and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to classify radio signals given any 2D Spectogram of radio signal input images.
Deep Learning
Convolutional Neural Network
pytorch
classification
Computer Vision
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Introduction
Configurations
Declare Spec Augmentations
Create Custom Dataset
Load Dataset into Batches
Create Model
Create Train and eval function
Training Loop
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
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You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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