Chapter 4: Deep Recurrent Model Architectures
Neural networks are powerful machine learning tools that are used to help us learn complex patterns between the inputs (X) and outputs (y) of a dataset. In the previous chapter, we discussed convolutional neural networks, which learn a one-to-one mapping between X and y; that is, each input, X, is independent of the other inputs and each output, y, is independent of the other outputs of the dataset.
In this chapter, we will discuss a class of neural networks that can model sequences where X (or y) is not just a single independent data point, but a temporal sequence of data points [X1, X2, .. Xt] (or [y1, y2, .. yt]). Note that X2 (which is the data point at time step 2) is dependent on X1, X3 is ...
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