In this chapter, we introduced Convolutional Neural Networks (CNNs).
We have seen how the architecture of these networks yield CNNs, which are particularly suitable for image classification problems, making the training phase faster and the test phase more accurate.
We have therefore implemented an image classifier, testing it on MNIST dataset, where have achieved a 99 percent accuracy.
Finally, we built a CNN to classify emotions starting from a dataset of images; we tested the network on a single image and we evaluated the limits and the goodness of our model.
The next chapter describes autoencoders, these algorithms are useful for dimensionality reduction, classification, regression, collaborative filtering, feature learning and topic modeling. We will carry out further data analysis using autoencoders and measure classification performance using image datasets.
