Building blocks of convolutional neural networks
Convolutional neural networks, or CNNs, are a family of models that were inspired by how the visual cortex of human brain works when recognizing objects.
The development of CNNs goes back to the 1990's, when Yann LeCun and his colleagues proposed a novel neural network architecture for classifying handwritten digits from images (Handwritten Digit Recognition with a Back-Propagation Network, Y LeCun, and others, 1989, published at Neural Information Processing Systems.(NIPS) conference).
Due to the outstanding performance of CNNs for image classification tasks, they have gained a lot of attention and this led to tremendous improvements in machine learning and computer vision applications.
In the following sections, we next see how CNNs are used as feature extraction engines, and then we'll delve into the theoretical definition of convolution and computing convolution in one and two dimensions.
