Welcome to the InfoSci Platform
Deep Learning Theory and Software

Deep Learning Theory and Software

Copyright: © 2020 |Pages: 39
ISBN13: 9781799815549|ISBN10: 1799815544|ISBN13 Softcover: 9781799815556|EISBN13: 9781799815563
DOI: 10.4018/978-1-7998-1554-9.ch002
Cite Chapter Cite Chapter

MLA

Jiann-Ming Wu and Chao-Yuan Tien. "Deep Learning Theory and Software." MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities, IGI Global, 2020, pp.23-61. https://doi.org/10.4018/978-1-7998-1554-9.ch002

APA

J. Wu & C. Tien (2020). Deep Learning Theory and Software. IGI Global. https://doi.org/10.4018/978-1-7998-1554-9.ch002

Chicago

Jiann-Ming Wu and Chao-Yuan Tien. "Deep Learning Theory and Software." In MatConvNet Deep Learning and iOS Mobile App Design for Pattern Recognition: Emerging Research and Opportunities. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1554-9.ch002

Export Reference

Mendeley
Favorite

Abstract

In the past decade, deep learning has achieved a significant breakthrough in development. In addition to the emergence of convolution, the most important is self-learning of deep neural networks. By self-learning methods, adaptive weights of kernels and built-in parameters or interconnections are automatically modified such that the error rate is reduced along the learning process, and the recognition rate is improved. Emulating mechanism of the brain, it can have accurate recognition ability after learning. One of the most important self-learning methods is back-propagation (BP). The current BP method is indeed a systematic way of calculating the gradient of the loss with respect to adaptive interconnections. The main core of the gradient descent method addresses on modifying the weights negatively proportional to the determined gradient of the loss function, subsequently reducing the error of the network response in comparison with the standard answer. The basic assumption for this type of the gradient-based self-learning is that the loss function is the first-order differential.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.