Recursive Neural Networks with PyTorch Parallel Forall

From Siri to Google Translate, deep neural networks have enabled breakthroughs in machine understanding of natural language. Most of these models treat language as a flat sequence of words or characters, and use a kind of model called a recurrent neural network (RNN) to process this sequence. But many linguists think that language is best understood as a hierarchical tree of phrases, so a significant amount of research has gone into deep learning models known as recursive neural networks that take this structure into account. While these models are notoriously hard to implement and inefficient to run, a brand new deep learning framework called PyTorch makes these and other complex natural language processing models a lot easier. While recursive neural networks are a good demonstration of PyTorch's flexibility, it is also a fully-featured framework for all kinds of deep learning with particularly strong support for computer vision.