Difference between revisions of "(Tree) Recursive Neural (Tensor) Network (RNTN)"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [ | + | [https://www.youtube.com/results?search_query=recursive+recurrent+neural+tree+tensor+network YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=recursive+recurrent+neural+tree+tensor+network+machine+learning+ML+artificial+intelligence ...Google search] |
| − | * [ | + | * [https://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen] |
Certain patterns are innately hierarchical, like the underlying parse tree of a natural language sentence. A Recursive Neural Tensor Network (RNTN) is a powerful tool for deciphering and labelling these types of patterns. Structurally, an RNTN is a binary tree with three nodes: a root and two leaves. The root and leaf nodes are not neurons, but instead they are groups of neurons – the more complicated the input data the more neurons are required. As expected, the root group connects to each leaf group, but the leaf groups do not share a connection with each other. Despite the simple structure of the net, an RNTN is capable of extracting deep, complex patterns out of a set of data. An RNTN detects patterns through a recursive process. In a sentence-parsing application where the objective is to identify the grammatical elements in a sentence (like a noun phrase or a verb phrase, for example), the first and second words are initially converted into an ordered set of numbers known as a vector. | Certain patterns are innately hierarchical, like the underlying parse tree of a natural language sentence. A Recursive Neural Tensor Network (RNTN) is a powerful tool for deciphering and labelling these types of patterns. Structurally, an RNTN is a binary tree with three nodes: a root and two leaves. The root and leaf nodes are not neurons, but instead they are groups of neurons – the more complicated the input data the more neurons are required. As expected, the root group connects to each leaf group, but the leaf groups do not share a connection with each other. Despite the simple structure of the net, an RNTN is capable of extracting deep, complex patterns out of a set of data. An RNTN detects patterns through a recursive process. In a sentence-parsing application where the objective is to identify the grammatical elements in a sentence (like a noun phrase or a verb phrase, for example), the first and second words are initially converted into an ordered set of numbers known as a vector. | ||
Latest revision as of 19:46, 27 March 2023
YouTube search... ...Google search
Certain patterns are innately hierarchical, like the underlying parse tree of a natural language sentence. A Recursive Neural Tensor Network (RNTN) is a powerful tool for deciphering and labelling these types of patterns. Structurally, an RNTN is a binary tree with three nodes: a root and two leaves. The root and leaf nodes are not neurons, but instead they are groups of neurons – the more complicated the input data the more neurons are required. As expected, the root group connects to each leaf group, but the leaf groups do not share a connection with each other. Despite the simple structure of the net, an RNTN is capable of extracting deep, complex patterns out of a set of data. An RNTN detects patterns through a recursive process. In a sentence-parsing application where the objective is to identify the grammatical elements in a sentence (like a noun phrase or a verb phrase, for example), the first and second words are initially converted into an ordered set of numbers known as a vector.
