Projects per year

### Abstract

Multilayered artificial neural networks are becoming a pervasive tool in a host of application fields. At the heart of this deep learning revolution are familiar concepts from applied and computational mathematics; notably, in calculus, approximation theory, optimization and linear algebra. This article provides a very brief introduction to the basic ideas that underlie deep learning from an applied mathematics perspective. Our target audience includes postgraduate and final year undergraduate students in mathematics who are keen to learn about the area. The article may also be useful for instructors in mathematics who wish to enliven their classes with references to the application of deep learning techniques. We focus on three fundamental questions: what is a deep neural network? how is a network trained? what is the stochastic gradient method? We illustrate the ideas with a short MATLAB code that sets up and trains a network. We also show the use of state-of-the art software on a large scale image classification problem. We finish with references to the current literature.

Original language | English |
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Pages (from-to) | 860–891 |

Number of pages | 32 |

Journal | SIAM Review |

Volume | 61 |

Issue number | 4 |

DOIs | |

Publication status | Published - 6 Nov 2019 |

### Keywords

- back progagation
- chain rule
- convolution
- image classification
- neural network
- overfitting
- sigmoid
- stochastic gradient method

## Fingerprint Dive into the research topics of 'Deep learning: an introduction for applied mathematicians'. Together they form a unique fingerprint.

## Projects

- 2 Finished

## Data Analytics for Future Cities

Higham, D.

EPSRC (Engineering and Physical Sciences Research Council)

1/01/15 → 31/12/19

Project: Research Fellowship

## UK Quantum Technology Hub in Quantum Enhanced Imaging (Quantic)

Birch, D., Dawson, M., Dawson, M., Strain, M., Chen, Y., Gu, E., Li, D., Strain, M., Watson, I., Jeffers, J., Oppo, G. & Yao, A.

EPSRC (Engineering and Physical Sciences Research Council), HORIBA Jobin Yvon IBH Ltd

1/10/14 → 31/10/19

Project: Research

## Cite this

Higham, C. F., & Higham, D. J. (2019). Deep learning: an introduction for applied mathematicians.

*SIAM Review*,*61*(4), 860–891. https://doi.org/10.1137/18M1165748