Deep Learning and Machine Learning for Signal Processing Applications
Deep Learning and Machine Learning are powerful tools to build applications for signals and time-series data across a broad range of industries. These applications range from predictive maintenance and health monitoring to financial portfolio forecasting and advanced driver assistance systems.
In this session, through detailed examples we will showcase several techniques and apps in MATLAB to build predictive models for real-life applications. We will cover how to build your signal datasets, label your signals using apps, and preprocess the data. We will explore various feature extraction techniques that help to create robust and accurate AI models. We will also examine what are the key types of networks used for deep learning and how they are applied and how the trained models can be deployed on embedded hardware.
- Easily manage signal datasets using datastores
- Using Signal Labeler and Signal Analyzer App for AI workflows
- Feature extraction techniques including AutoML techniques such as wavelet scattering and time-frequency representations
- Acceleration of training using GPUs and deployment on embedded hardware like Raspberry Pi
About the Presenter
Esha Shah is a Product Manager at MathWorks focusing on Signal Processing and Wavelets Toolbox. She supports MATLAB users focusing on advanced signal processing and AI workflows. Before joining MathWorks, she received her Master’s in Engineering Management from Dartmouth College and Bachelor’s in Electronics and Telecommunication Engineering from Pune University, India
Recorded: 24 Feb 2021
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