Neural structured learning is an easy-to-use, open-sourced TensorFlow framework that both novice and advanced developers can use for training neural networks with structured signals. NSL can be applied to construct accurate and robust models for vision, language understanding, and prediction in general.

Many machine learning tasks benefit from using structured data that contains rich relational information among the samples. These structures can be explicitly given (e.g., as a graph) or implicitly inferred (e.g., as an adversarial example). Leveraging structured signals during training allows developers to achieve higher model accuracy, particularly when the amount of labeled data is relatively small. Training with structured signals also leads to more robust models.

Da-Cheng Juan and Sujith Ravi explore the concept, framework, and workflow of NSL and provides the code examples for practitioners and developers.

Prerequisite knowledge

• A basic understanding of neural networks and TensorFlow

What you'll learn

• Discover the concept, framework, and workflow of NSL