Machine Learning with Neural Networks

Artificial neural networks provide a distributed computing technology that can be trained to approximate any computable function, and have enabled substantial advances in areas such as computer vision, robotics, speech recognition and natural language processing. This chapter provides an introduction to Artificial Neural Networks, with a review of the early history of perceptron learning. It presents a mathematical notation for multi-layer neural networks and shows how such networks can be iteratively trained by back-propagation of errors using labeled training data. It derives the back-propagation algorithm as a distributed form of gradient descent that can be scaled to train arbitrarily large networks given sufficient data and computing power. Learning Objectives: This chapter provides an introduction to the training and use of Artificial Neural Networks, and prepares students to understand fundamental concepts of deep-learning that are described and used in later chapters.