Machine learning architectures based on convolutional neural networks (CNNs) have proved to be highly valuable for a wide range of applications, ranging from computer vision to the analysis of images and the processing or generation of human language. To tackle more advanced tasks, however, these architectures are becoming increasingly complex and computationally demanding.
This article was originally published on this website.
