Convolutional neural networks. In my introductory post on neural networks, I introduced the concept of a neural network that looked something like this. As it turns out, there are many different neural network architectures, each with its own set of benefits. The architecture is defined by the type of layers we implement and how layers are connected together. The neural network above is known as a feed-forward network(also known as a multilayer perceptron) where we simply have a series of fully-connected layers. Today, I'll be talking about convolutional neural networkswhich are used heavily in image recognition applications of machine learning. Convolutional neural networks provide an advantage over feed-forward networks because they are capable of considering locality of features. Consider the case where we'd like to build an neural network that could recognize handwritten digits. For example, given the following 4 by 4 pixel image as input, our neural network should classify it as a "1".