From the course: Deep Learning Foundations: Natural Language Processing with TensorFlow
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Introduction to RNNs - TensorFlow Tutorial
From the course: Deep Learning Foundations: Natural Language Processing with TensorFlow
Introduction to RNNs
- [Instructor] So far we have done text classification by understanding the positive and negative sentiment in news headlines and movie reviews, used to pass the data and labels to a simple neural network to learn the function that classifies text as positive or negative. Now the model didn't do as well as one would have expected. The main reason for this was that the context of words was hard to follow when the words were broken down into sub words. So the sequence in which the tokens for the sub words appear become very important in understanding the meaning. In order to find the solution to this problem, let's first understand how sequential data influences the output. So here we have a factorial data. Where n factorial can be written as follows. You have n factorial which can be written as n into n minus one into n minus two, so on up till one. And similarly if you want to write n minus one factorial, you can write it…
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