Introducing CNNs

In this section, you will learn about CNNs. Specifically, you will first get an understanding of the sort of operations present in a CNN, such as convolution layers, pooling layers, and fully connected layers. Next, we will briefly see how all of these are connected to form an end-to-end model.

It is important to note that the first use case we’ll be solving with CNNs is an image classification task. CNNs were originally used to solve computer vision tasks and were adopted for NLP much later. Furthermore, CNNs have a stronger presence in the computer vision domain than the NLP domain, making it easier to explain the underlying concepts in a vision context. For this reason, we will first learn how CNNs are used in computer vision and then move on to NLP.

CNN fundamentals

Now, let’s explore the fundamental ideas behind a CNN without delving into too much technical detail. A CNN is a stack of layers, such as convolution layers, pooling layers...