우리 학부의 장동의교수가 옥스포드대학의 Anthony L. Caterini와 함께 "Deep Neural Networks in a Mathematical Framework”라는 제목의 딥러닝에 관한 책을 Springer출판사를 통하여 출간하였습니다. 책의 자세한 정보는 아래에 있습니다. 책의 eBook은 링크 https://doi.org/10.1007/978-3-319-75304-1 를 통하여 원내에서 다운로드 받으실 수 있고, 제본된 책은 online/offline 서점에서 이용하실 수 있습니다.
Title: Deep Neural Networks in a Mathematical Framework
Authors: Anthony L. Caterini and Dong Eui Chang
Publisher: Springer; 2018
ISBN 978-3-319-75304-1 (eBook)
Book cover and Front Matter: in attachment
This book describes how to build a rigorous end-to-end mathematical framework for deep neural networks. The authors provide tools to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive gradient descent algorithms in a unified way for several neural network structures, including multilayer perceptrons, convolutional neural networks, deep autoencoders and recurrent neural networks. Furthermore, the authors developed framework is both more concise and mathematically intuitive than previous representations of neural networks.
This book is one-step towards unlocking the black box of Deep Learning. The authors believe that this framework will help catalyze further discoveries regarding the mathematical properties of neural networks. This book is accessible not only to researchers, professionals and students working and studying in the field of deep learning, but also to those outside of the neutral network community.