Anomaly detection is the process of identifying events or patterns that deviate from expected behavior. This is important for applications such as predictive maintenance, but can be difficult to achieve through inspection alone. Machine learning and deep learning (AI) techniques for anomaly detection can uncover anomalies in time series or image data that would otherwise be difficult to detect. Learn how and why to apply anomaly detection algorithms to identify anomalies in hardware sensor data.

Check out these other links:
– What is anomaly detection?: https://bit.ly/3Re46SO
– What is automated visual inspection?: https://bit.ly/3fn3LQj
– Time series anomaly detection using deep learning (example): https://bit.ly/3BFY6MS
– Want to see all references in one clear list? Check out this journey on Resourcium: https://bit.ly/3SrCI4Y

00:00 What is anomaly detection?
01:17 What is anomaly detection used for?
03:10 How anomaly detection works
03:47 Machine learning techniques for time series data
05:00 Applying autoencoders to hardware for anomaly detection
08:55 Training and testing algorithms on hardware

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