Overview

Anomaly detection is a process in machine learning that identifies data points, events, or observations in time-series data that deviate from the majority of the data and does not conform to normal behavior. To build an anomaly detection solution for time series data, a cognitive IoT solution, you can use Keras and TensorFlow to create an unsupervised deep learning neural network for anomaly detection on time-series data.

This learning path will introduce deep learning and long-short term memory networks (neural networks) and autoencoders, show you how to create a test physical model based data generator for anomaly detection, and finally show you how to create a deep learning neural network for anomaly detection on time-series data using Keras and TensorFlow.

Skill level

Intermediate.

Estimated time to complete

Approximately 2 hours

Learning objectives

Upon completion of this learning path, you will:

Understand how deep learning and an LSTM network can outperform state-of-the-art anomaly detection algorithms on time-series sensor data – or any type of sequence data in general.
Use Node-RED and the Lorenz Attractor Model to generate realistic test data by sampling from a physical model.
How to use TensorFlow to accelerate linear algebra operations by optimizing executions and understand how Keras provides an accessible framework on top of TensorFlow.
Create an unsupervised LSTM autoencoder neural network using TensorFlow and Keras.
How to containerize that neural network and put it 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

Developing cognitive IoT solutions for anomaly detection using deep learning

Summary