Deep Learning For Time Series Data

Version 1.0.2 (1,86 MB) von MathWorks Student Competitions Team
The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data.
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Aktualisiert 23 Nov 2020
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The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data. The first way is using continuous wavelet transform and transfer learning, whereas the second way is using Wavelet Scattering and LSTMs. The explanations of the code are in Chinese. The used data set can be download on：https://github.com/mathworks/physionet_ECG_data/

The video series (in Chinese) on this topic can be found as follows:
https://www.mathworks.com/videos/series/deep-learning-for-time-series-data.html

Zitieren als

MathWorks Student Competitions Team (2024). Deep Learning For Time Series Data (https://github.com/mathworks/deep-learning-for-time-series-data/releases/tag/v1.0.2), GitHub. Abgerufen .

Kompatibilität der MATLAB-Version
Erstellt mit R2020a
Kompatibel mit R2020a bis R2020b
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Version Veröffentlicht Versionshinweise
1.0.2

See release notes for this release on GitHub: https://github.com/mathworks/deep-learning-for-time-series-data/releases/tag/v1.0.2
1.0.1

See release notes for this release on GitHub: https://github.com/mathworks/deep-learning-for-time-series-data/releases/tag/v1.0.1
1.0

Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.
Um Probleme in diesem GitHub Add-On anzuzeigen oder zu melden, besuchen Sie das GitHub Repository.