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. Subscribe: http://bit.ly/venelin-subscribe Complete tutorial + source code: https://www.curiousily.com/posts/time-series-anomaly-detection-using-lstm-autoencoder-with-pytorch-in-python/ GitHub: https://github.com/curiousily/Getting-Things-Done-with-Pytorch 📖 Read Hacker's Guide to Machine Learning with Python: http://bit.ly/Hackers-Guide-to-Machine-Learning-with-Python ⭐️ 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 #TimeSeries #AnomalyDetection #LSTMAutoencoder #PyTorch #Python