deep neural network

Terms from Artificial Intelligence: humans at the heart of algorithms

Page numbers are for draft copy at present; they will be replaced with correct numbers when final book is formatted. Chapter numbers are correct and will not change now.

A deep neural network is a neural network with lots of layers.. Tyoically the layers are also large, in the sense of lots of nodes. Each layer can also have a differnt size and use different types of learning. For example, it is common for the first layer to be a restricted Boltzmann machine in order to perform dimensional reduction. Typically the inner laters are underdetermined (many equally good arrangements of weightings); this and theor disrance from the output layer means that backpropagation or similar learning rules need to have very slow learning rates to avoid instabilities. In addition, more layers and laregr layers usually requires more training data. Together these mean that deep learning (training of deep neural networks) requires. a lot of training data. This combination of computational cost and data volume is one of the main reasons that the use deep neural networks were not widely adopted for many years.

Used in Chap. 1: page 7; Chap. 6: pages 87, 89, 90, 91, 92; Chap. 7: page 106; Chap. 8: pages 111, 112, 113, 114, 115, 124; Chap. 9: page 139; Chap. 11: pages 160, 169, 170, 172; Chap. 12: pages 196, 197, 198; Chap. 13: pages 217, 218; Chap. 14: pages 229, 234; Chap. 17: page 285; Chap. 18: pages 302, 309; Chap. 19: pages 322, 330; Chap. 20: pages 335, 337; Chap. 21: pages 351, 352, 355, 357, 360, 362; Chap. 22: pages 365, 368; Chap. 23: pages 392, 393; Chap. 24: page 399

Also known as deep learning

Deep learning architecture – multiple layers, with varying connection topologies