In this chapter, we introduced convolutional networks, a specialized neural network architecture that has taken cues from our (limited) understanding of human vision, and performs particularly well on grid-like data. We covered the central operation of convolution or cross-correlation that drives the discovery of filters, which, in turn, detects features that are useful for solving the task at hand.
We reviewed several state-of-the-art architectures that are good starting points, especially because transfer learning enables us to reuse pre-trained weights, and reduce the otherwise rather computationally and data-intensive training effort. We also saw that Keras makes it relatively straightforward to implement and train a diverse set of deep CNN architectures.
In the next chapter, we will turn our attention to recurrent neural networks (RNNs), which are designed specifically...