Chapter 11. Using Convolutional and Recurrent Methods for Sequence Models
The last few chapters introduced you to sequence data. You saw how to predict it first using statistical methods, then basic machine learning methods with a deep neural network. You also explored how to tune the model’s hyperparameters using Keras Tuner. In this chapter, you’ll look at additional techniques that may further enhance your ability to predict sequence data using convolutional neural networks as well as recurrent neural networks.
Convolutions for Sequence Data
In Chapter 3 you were introduced to convolutions where a 2D filter was passed over an image to modify it and potentially extract features. Over time, the neural network learned which filter values were effective at matching the modifications made to the pixels to their labels, effectively extracting features from the image. The same technique can be applied to numeric time series data, but with one modification: the convolution will be one-dimensional instead of two-dimensional.
Consider, for example, the series of numbers in Figure 11-1.
Figure 11-1. A sequence of numbers
A 1D convolution could operate on these as follows. Consider the convolution to be a 1 × 3 filter with filter values of –0.5, 1, and –0.5, respectively. In this case, the first value in the sequence will be lost, and the second value will be transformed from 8 to –1.5, ...
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