3.2 Convolutional Neural Networks
Convolutional neural networks (CNNs) are a specialized class of deep neural architecture consisting
of one or more pairs of alternating convolutional and pooling layers. A convolution layer applies
filters that process small local parts of the input, where these filters are replicated along the whole

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input space. A pooling layer converts convolution layer activations to low resolution by taking the
maximum filter activation within a specified window and shifting across the activation map. CNNs
are variants of fully connected neural networks widely used for processing data with grid-like
topology. For example, time-series data (1D grid) with samples at regular intervals or images (2D
grid) with pixels constitute a grid-like structure.
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