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James Price

James Price

3 years ago

Use real-world Electrocardiogram (ECG) data to detect anomalies in a patient heartbeat. We’ll build an LSTM Autoencoder, train it on a set of normal heartbeats and classify unseen examples as normal or anomalies.

⭐️ Tutorial Contents ⭐️

(04:35​) Load the ECG data
(14:09​) Exploratory Data Analysis
(23:29​) Data preprocessing
(33:30​) Build an LSTM Autoencoder with PyTorch
(43:07​) Training
(50:58​) Loading pre-trained model
(51:53​) Choosing a threshold for anomaly detection
(55:36​) Finding abnormal heartbeats

Complete tutorial + source code: https://www.curiousily.com/posts/time… ​

GitHub: https://github.com/curiousily/Getting…

Subscribe: https://www.youtube.com/c/VenelinValkovBG/featured

#pytorch #python

Time Series Anomaly Detection Tutorial with PyTorch in Python | LSTM Autoencoder for ECG Data

youtube.com

Time Series Anomaly Detection Tutorial with PyTorch in Python | LSTM Autoencoder for ECG Data

Use real-world Electrocardiogram (ECG) data to detect anomalies in a patient heartbeat. We'll build an LSTM Autoencoder, train it on a set of normal heartbeats and classify unseen examples as normal or anomalies.

8.10 GEEK