| 书目名称 | Recurrent Neural Networks | | 副标题 | From Simple to Gated | | 编辑 | Fathi M. Salem | | 视频video | | | 概述 | Explains the intricacy and diversity of recurrent networks from simple to more complex gated recurrent neural networks.Discusses the design framing of such networks, and how to redesign simple RNN to | | 图书封面 |  | | 描述 | .This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author’s approach enables strategic co-trainingof output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled | | 出版日期 | Textbook 2022 | | 关键词 | Neural Networks textbook; Deep Learning textbook; Embedded Deep Learning; Neural Networks and Deep Lear | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-89929-5 | | isbn_softcover | 978-3-030-89931-8 | | isbn_ebook | 978-3-030-89929-5 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
The information of publication is updating
|
|
发表于 2025-3-21 17:39:26
