Deep learning (DL) networks are composed of multiple processing layers that learn data representations with multiple levels of abstraction. In recent years, DL networks have significantly improved the state of the art across different domains, including speech processing, text mining, pattern recognition, object detection, robotics, and big data analytics. Generally, a researcher or practitioner who is planning to use DL networks for the first time faces difficulties in selecting suitable software tools. The present article provides a comprehensive list and taxonomy of current programming languages and software tools that can be utilized for implementation of DL networks. The motivation of this article is hence to create awareness among researchers, especially beginners, regarding the various languages and interfaces that are available to implement deep learning and to provide a simplified ontological basis for selecting between them.
|Title of host publication||Advances in Intelligent Systems and Computing|
|Publication status||E-pub ahead of print - 29 Sept 2018|