Summary

In this chapter, we learned the basic concepts of DL and discovered how to implement a CNN algorithm in the MATLAB environment. First, we looked at how DL enables automated feature extraction, then we looked at how to train a deep network, and then we got a taste of the most popular DL architectures.

We then focused on CNN to analyze it in detail. We learned about the different layers that make up this network and what functions these layers perform. We then saw in practice how to implement a CNN in the MATLAB environment for image classification of pistachio nuts. We learned how to correctly import the image database, how to draw the architecture of the network with the different layers one after the other, and how to set the network parameters. Finally, we saw how to use evaluation metrics for the correct interpretation of the results.

In the last section, we introduced some of the most used networks, which will be the subject of more detailed study and application...