{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-an-introduction-for-applied","title":"Deep Learning: An Introduction for Applied Mathematicians","arxiv_id":"1801.05894","date":"2018-01-17","proceeding":null,"authors":["Catherine F. Higham","Desmond J. Higham"],"abstract":"Multilayered artificial neural networks are becoming a pervasive tool in a\nhost of application fields. At the heart of this deep learning revolution are\nfamiliar concepts from applied and computational mathematics; notably, in\ncalculus, approximation theory, optimization and linear algebra. This article\nprovides a very brief introduction to the basic ideas that underlie deep\nlearning from an applied mathematics perspective. Our target audience includes\npostgraduate and final year undergraduate students in mathematics who are keen\nto learn about the area. The article may also be useful for instructors in\nmathematics who wish to enliven their classes with references to the\napplication of deep learning techniques. We focus on three fundamental\nquestions: what is a deep neural network? how is a network trained? what is the\nstochastic gradient method? We illustrate the ideas with a short MATLAB code\nthat sets up and trains a network. We also show the use of state-of-the art\nsoftware on a large scale image classification problem. We finish with\nreferences to the current literature.","url_abs":"http://arxiv.org/abs/1801.05894v1","url_pdf":"http://arxiv.org/pdf/1801.05894v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-an-introduction-for-applied","repo_url":"https://github.com/foolpanda/deepFC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deep-learning-an-introduction-for-applied","repo_url":"https://github.com/jamesrynn/Basic_DNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}