NLP with TensorFlow
Text data is inherently sequential, defined by the order in which words occur. Words follow one another, building upon previous ideas and shaping those to come. Understanding the sequence of words and the context in which they are applied is straightforward for humans. However, this poses a significant challenge to feed-forward networks such as convolutional neural networks (CNNs) and traditional deep neural networks (DNNs). These models treat text data as independent inputs; hence, they miss the interconnected nature and flow of language. For example, let’s take the sentence “The cat, which is a mammal, likes to chase mice." Humans immediately recognize the relationship between the cat and mice, as we process the entire sentence as a whole and not individual units.
A recurrent neural network (RNN) is a type of neural network designed to handle sequential data such as text and time-series data. When working with text data, RNNs’ memory...
