Developing predictive models for signal, time-series, and text data using artificial intelligence (AI) techniques is growing in popularity across a variety of applications and industries, including speech classification, radar target classification, physiological signal recognition, and sentiment analysis.
In this talk, you will learn how MATLAB® empowers engineers and scientists to apply deep learning beyond the well-established vision applications. You will see demonstrations of advanced signal and audio processing techniques such as automated feature extraction using wavelet scattering and expanded support for ground truth labeling. The talk also shows how MATLAB covers other key elements of the AI workflow:
- Use of signal preprocessing techniques and apps to improve the accuracy of predictive models
- Use of transfer learning and wavelet analysis for radar target and ECG classification
- Interoperability with other deep learning frameworks through importers and ONNX™ converter for collaboration in the AI ecosystem
- Scalability of computations with GPUs, multi-GPUs, or on the cloud
