Neural Networks and Deep Learning
Hongming Wang, Ryszard Czermiński, Andrew C. Jamieson
Abstract
Abstract Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.
Topics & Concepts
Deep learningComputer scienceArtificial intelligenceArtificial neural networkKey (lock)Field (mathematics)Class (philosophy)Deep neural networksArchitectureData scienceMachine learningGeographyMathematicsArchaeologyComputer securityPure mathematicsNeural Networks and ApplicationsData Stream Mining TechniquesStochastic Gradient Optimization Techniques