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About CS 224D: Deep Learning For NLP: Lecture Notes: Part IV Spring 2015
This document provides an overview of recurrent neural networks (RNNs) for natural language processing tasks. It discusses how RNNs can model sequences of words by incorporating information from all previous words through a hidden state. In each time step, the RNN takes the current word and previous hidden state as input to produce a predicted next word and updated hidden state. This allows RNNs to consider an unlimited history of previous words, unlike traditional language models that are limited to a fixed window size. The document also covers common RNN architectures like LSTMs and describes challenges like vanishing gradients that earlier RNNs faced.
Detailed Information
| Author: | ['Kumar J'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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