Data Science in the Wolfram Language
Abstract
The Wolfram Language provides a unique environment for doing data science: highly automated machine learning, a neural network framework that is built into the language itself, easy cloud deployment, and powerful symbolic mathematical capabilities. The focus of this talk will be on the neural network framework. The first aim of the framework is to meld automation, flexibility, and scalability. Specifics will be discussed, such as automating the process of efficiently training networks on variable-length sequences. The second aim of the framework is to provide easy access to the widest possible set of pre-trained models, first by curated conversion of existing models from other frameworks (Caffe, TensorFlow, MXNet, Torch, DarkNet, etc), and second by a large-scale effort to build 30+ user-facing functions (e.g. ImageIdentify, ImageColorize, LanguageTranslate, etc) using the network framework and exposing these trained networks to users. This effort involves a major curation, data management and training challenge.
