Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

And then let’s visualize the first ten squares:
fig = plot_data(points, directions)
Figure 8.4 - Sequence dataset
The corners show the order in which they were drawn. In the first square, the
drawing started at the top-right corner (corresponding to the blue C corner) and
followed a clockwise direction (corresponding to the CDAB sequence).
In the next chapter, we’ll use the first two corners to predict the
other two, so the model will need to learn not only the direction
but also the coordinates. We’ll build a sequence-to-sequence
model that uses one sequence to predict another.
For now, we’re sticking to classifying the direction, given all four data points of a
given square. But, first, we need to introduce…
Recurrent Neural Networks (RNNs)
Recurrent neural networks are perfectly suited for sequence problems since they
take advantage of the underlying structure of the data, namely, the order of the
data points. We’ll see in great detail how the data points, sequentially presented to
a recurrent neural network, modify the RNN’s internal (hidden) state, which will
ultimately be a representation of the full sequence.
SPOILER ALERT: Recurrent neural networks are all about
producing a hidden state that best represents a sequence.
Recurrent Neural Networks (RNNs) | 591