Summary
In this chapter, we discussed CNNs and their various applications. First, we went through a detailed explanation about what CNNs are and their ability to excel at machine learning tasks. Next we decomposed the CNN into several components, such as convolution and pooling layers, and discussed in detail how these operators work. Furthermore, we discussed several hyperparameters that are related to these operators such as filter size, stride, and padding. Then, to illustrate the functionality of CNNs, we walked through a simple example of classifying images of handwritten digits to the corresponding image. We also did a bit of analysis to see why the CNN fails to recognize some images correctly. Finally, we started talking about how CNNs are applied for NLP tasks. Concretely, we discussed an altered architecture of CNNs that can be used to classify sentences. We then implemented this particular CNN architecture and tested it on an actual sentence classification task.
In the next chapter...
