Overview

Do you know that the data coming from various sources is very noisy and unstructured? 

Time-consuming and difficult to process the data? 

Discover how apps in MATLAB can be used to do data preparation more effectively.

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.

Highlights:

In this webinar, you will be able to learn,

• 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