Deep neural networks have a unique capability that can drive machine learning breakthroughs in understanding natural language processing processes. It was observed that most of these models treat language as a sequence of words or characters and use a model called Recurrent Neural Network (RNN).
Many researchers have concluded that language is best understood as corresponding to a hierarchical tree of phrases. This type includes recurrent neural networks that take into account specific structures.
PyTorch has specific features that help make these complex natural language processing models easier. It is a fully functional deep learning framework that strongly supports various deep learning tasks such as computer vision.
Characteristics of Recurrent Neural Networks include:
- Recurrent Neural Networks are designed to apply the same set of weights and have different graph structures.
- Nodes are traversed in topological order.
- Such networks are trained by automatic differentiation in reverse mode.
- Natural language processing includes special cases of recurrent neural networks.
- This recurrent neural tensor network includes various combined functional nodes in a tree.
An example of Recurrent Neural Network is demonstrated below −
