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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
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A recursive neural network is created in such a way that it includes applying same set of weights with different graph like structures.
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The nodes are traversed in topological order.
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This type of network is trained by the reverse mode of automatic differentiation.
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Natural language processing includes a special case of recursive neural networks.
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This recursive neural tensor network includes various composition functional nodes in the tree.
The example of recursive neural network is demonstrated below −
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