In this chapter, we created an LSTM-based neural network that can predict the sentiment of movie reviews with 85% accuracy. We first looked at the theory behind recurrent neural networks and LSTMs, and we understood that they are a special class of neural network designed to handle sequential data, where the order of the data matters.
We also looked at how we can convert sequential data such as a paragraph of text into a numerical vector, as input for neural networks. We saw how word embeddings can reduce the dimensionality of such a numerical vector into something more manageable for training neural networks, without necessarily losing information. A word embedding layer does this by learning which words are similar to one another, and it places such words in a cluster, in the transformed vector.
We also looked at how we can easily construct a LSTM neural network in...
