Graph Gurus Episode 19: Deep Learning Implemented by GSQL on a Native Parallel Graph Database

Deep learning, as a class of artificial intelligence technology, has demonstrated its potential to produce results superior to human experts over a broad range of applications such as computer vision, speech recognition, autonomous driving, recommendation systems, and drug design. Unlike many machine learning methods that usually require the learning features provided to the model manually, deep learning utilizes multiple-layer artificial neural network to progressively extract higher level features from the raw input which allows it to automatically discover the features to be used for classification. Considering the ever growing size of the neural network model and the training data as well as the increasing complexity of the graphs to represent the neural networks, a graph database management system shows great advantages in scaling to large models and representing the neural network.