Representing words as vectors

So far, we have looked at what RNNs and LSTM networks represent. There remains an important question we need to address: how do we represent words as input data for our neural network? In the case of CNNs, we saw how images are essentially three-dimensional vectors/matrixes, with dimensions represented by the image width, height, and the number of channels (three channels for color images). The values in the vectors represent the intensity of each individual pixel.

One-hot encoding

How do we create a similar vector/matrix for words so that they can be used as input to our neural network? In earlier chapters, we saw how categorical variables such as the day of week can be one-hot encoded to numerical...