Deep Learning For Time Series Data

The examples showcase two ways of using deep learning for classifying time-series data, i.e. ECG data.

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Actualizado 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

Citar como

MathWorks Student Competitions Team (2023). Deep Learning For Time Series Data (https://github.com/mathworks/deep-learning-for-time-series-data/releases/tag/v1.0.2), GitHub. Recuperado .

Compatibilidad con la versión de MATLAB
Se creó con R2020a
Compatible con cualquier versión desde R2020a hasta R2020b
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Versión Publicado Notas de la versión
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

Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.
Para consultar o notificar algún problema sobre este complemento de GitHub, visite el repositorio de GitHub.