PyTorch - Recursive Neural Networks


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Deep neural networks have an exclusive feature for enabling breakthroughs in machine learning understanding the process of natural language. It is observed that most of these models treat language as a flat sequence of words or characters, and use a kind of model which is referred as recurrent neural network or RNN.

Many researchers come to a conclusion that language is best understood with respect to hierarchical tree of phrases. This type is included in recursive neural networks that take a specific structure into account.

PyTorch has a specific feature which helps to make these complex natural language processing models a lot easier. It is a fully-featured framework for all kinds of deep learning with strong support for computer vision.

Features of Recursive Neural Network

  • A recursive neural network is created in such a way that it includes applying same set of weights with different graph like structures.

  • The nodes are traversed in topological order.

  • This type of network is trained by the reverse mode of automatic differentiation.

  • Natural language processing includes a special case of recursive neural networks.

  • This recursive neural tensor network includes various composition functional nodes in the tree.

The example of recursive neural network is demonstrated below −

Recursive Neural Tensor Network
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