TensorFlow is already one of the most popular tools for creating deep learning models.

Google this week introduced Neural Structured Learning (NSL) to make this tool even better.

Here’s why, NSL is a big deal.

Neural Structured Learning in TensorFlow is an easy-to-use framework for training deep neural networks by leveraging structured signals along with feature inputs. This learning paradigm implements Neural Graph Learning in order to train neural networks using graphs and structured data. As the researchers mention, the graphs can come from multiple sources such as knowledge graphs, medical records, genomic data or multimodal relations. Moreover, this framework also generalises to adversarial learning.

This looks interesting.

Google today introduced Neural Structured Learning (NSL) , an open source framework that uses the Neural Graph Learning method for training neural networks with graphs and structured data. NSL works with with the TensorFlow machine learning platform and is made to work for both experienced and inexperienced machine learning […]