Data and Modeling in AI-Powered Signal Processing Applications
Expectations for signal processing applications are getting higher. Engineers need to create applications that can intelligently respond to inputs or make predictions; often, this means incorporating AI systems into their designs.
What does every AI-powered signal processing application need? A lot of representative signal data, a good network architecture (because signal data works particularly well with deep learning), and the right signal processing tools to turn that data into a source for automated learning.
This ebook covers:
The basics of deep learning for signal processing
Using datasets and labeling to train and validate models
Applying data augmentation and synthesis to improve the
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