Classify Radio Signals with PyTorch
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.
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- المدة 3 ساعات hours
- الطبع بواسطة Coursera Project Network
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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.الوحدات
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"Classify Radio Signals with PyTorch" is an engaging 2-hour guided project focused on using PyTorch and a state-of-the-art CNN model to classify radio signals from spectogram images. This intermediate-level course offers hands-on experience in spectogram augmentation, training loop functions, and utilizing the best-trained model for classification. Perfect for personal development, it's available for free on Coursera and ideal for learners looking to enhance their skills in signal processing and machine learning.
Parth Dhameliya