Summary

In this chapter, we introduced CNNs, a specialized NN 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 that in turn detect features useful to solve 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 pretrained 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 turn our attention to recurrent neural networks that are designed specifically for sequential data, such as time-series data, which is central to investment and trading.