Summary

Summary, next steps, and additional resources

Romeo Kienzler

Archived content

Archive date: 2023-07-07

This content is no longer being updated or maintained. The content is provided “as is.” Given the rapid evolution of technology, some content, steps, or illustrations may have changed.

Summary

In this learning path, you learned how to use Node-RED to generate realistic anomaly test data by sampling from a physical model, how to create an unsupervised LSTM autoencoder neural network using TensorFlow and Keras, and finally how that neural network can be containerized and put behind an HTTP service API for easy consumption and scaling on Docker, Kubernetes, and Knative using IBM Cloud Code Engine.

Developing cognitive IoT solutions for anomaly detection using deep learning

Summary

Summary

Creating a deep learning neural network for anomaly detection on time-series data