{"id":116798,"date":"2018-04-03T00:00:00","date_gmt":"2018-04-02T15:00:00","guid":{"rendered":"http:\/\/175.125.95.178\/ai-in-signal\/16798\/"},"modified":"2026-04-08T00:10:18","modified_gmt":"2026-04-07T15:10:18","slug":"16798","status":"publish","type":"ai-in-signal","link":"http:\/\/ee.presscat.kr\/en\/ai-in-signal\/16798\/","title":{"rendered":"&quot;Deep Neural Networks in a Mathematical Framework&quot; Published by Professor Dong-Eui Chang"},"content":{"rendered":"<p align=\"left\">Professor Dong-Eui Chang of our department has published a book on deep learning titled &#8220;Deep Neural Networks in a Mathematical Framework&#8221; with Anthony L. Caterni of Oxford University. (Springer; 2018) Detailed information about the book is on&nbsp;below. You can download eBooks through the link&nbsp;<a href=\"https:\/\/doi.org\/10.1007\/978-3-319-75304-1\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1007\/978-3-319-75304-1<\/a>, and bound books are available in online\/offline bookstores.<\/p>\n<p><strong>Title<\/strong>: Deep Neural Networks in a Mathematical Framework<\/p>\n<p><strong>Authors<\/strong>: Anthony L. Caterini and Dong Eui Chang<\/p>\n<p>Publisher: Springer; 2018<\/p>\n<p>ISBN 978-3-319-75303-4<\/p>\n<p>ISBN 978-3-319-75304-1 (eBook)<\/p>\n<p><a href=\"https:\/\/doi.org\/10.1007\/978-3-319-75304-1\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1007\/978-3-319-75304-1<\/a><\/p>\n<p><strong>Book cover and Front Matter<\/strong>: in attachment<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Summary<\/strong>:<\/p>\n<p align=\"left\">This book describes how to build a rigorous end-to-end mathematical framework&nbsp;for deep neural networks. The authors provide tools to represent and describe&nbsp;neural networks, casting previous results in the field in a more natural&nbsp;light. In particular, the authors derive gradient descent algorithms in a unified way&nbsp;for several neural network structures, including multilayer perceptrons,&nbsp;convolutional neural networks, deep autoencoders and recurrent neural&nbsp;networks. Furthermore, the authors developed framework is both more concise&nbsp;and mathematically intuitive than previous representations of neural&nbsp;networks.<\/p>\n<p align=\"left\">This book is one-step towards unlocking the&nbsp;<i>black box&nbsp;<\/i>of Deep&nbsp;Learning. The authors believe that this framework will help catalyze further discoveries&nbsp;regarding the mathematical properties of neural networks. This book is&nbsp;accessible not only to researchers, professionals and students working and&nbsp;studying in the field of deep learning, but also to those outside of the neutral&nbsp;network community.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>771<\/p>\n","protected":false},"featured_media":126706,"template":"","class_list":["post-116798","ai-in-signal","type-ai-in-signal","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-signal\/116798","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/ai-in-signal"}],"about":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/types\/ai-in-signal"}],"wp:featuredmedia":[{"embeddable":true,"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/media\/126706"}],"wp:attachment":[{"href":"http:\/\/ee.presscat.kr\/en\/wp-json\/wp\/v2\/media?parent=116798"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}