The purpose of convolutional layers, as mentioned previously are to extract features or details from an image. A complete CNN will have many convolutional layers. As a general trend, deeper layers will extract specific shapes for example eyes from an image, while shallower layers extract more general shapes like lines and curves. Pooling layers are used to reduce the size of the convolved map. This decreases the number of parameters needed to train and the computing power needed to train the model.

Click here to read the full story with my Friend Link! When I tell people that they should consider applying deep learning methods to their data, a common initial response I get is: (1) "I am not working with big enough data," and (2) "I do not have access to enough computational resources to train deep learning models." I believe these assumptions come from large companies (e.g. Google) that often like to show off by conducting research on large datasets, such as ImageNet which contains over a million pictures, and by using a large amount of GPUs. That's great for these companies, but from my impression, the average deep learning practitioner is not working with such large datasets and does not have access to such large computational resources.