Image Classification with Convolutional Neural Networks
Convolutional neural networks (CNNs) are the go-to algorithms when it comes to image classification. In the 1960s, neuroscientists Hubel and Wiesel conducted a study on the visual cortex in cats and monkeys. Their work unraveled how we visually process information in a hierarchical structure, showing how visual systems are organized into a series of layers where each layer is responsible for a different aspect of visual processing. This earned them a Nobel Prize, but more importantly, it served as the basis upon which CNNs are built. CNNs, by virtue of their nature, are well designed to work with data with spatial structures such as images.
However, in the early days, CNNs did not have the limelight due to a number of factors, such as insufficient training data, underdeveloped network architecture, insufficient computational resources, and the absence of modern techniques such as data augmentation and dropout. In the 2012 ImageNet...