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Titlebook: Computational Methods for Deep Learning; Theoretic, Practice Wei Qi Yan Textbook 20211st edition The Editor(s) (if applicable) and The Aut

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書目名稱Computational Methods for Deep Learning
副標(biāo)題Theoretic, Practice
編輯Wei Qi Yan
視頻videohttp://file.papertrans.cn/233/232712/232712.mp4
概述Introduce deep learning from mathematical viewpoint.Review mathematical methods in Bachelor and Master’s degree level.Detail mathematical approaches to resolve deep learning problems.Provide methodolo
叢書名稱Texts in Computer Science
圖書封面Titlebook: Computational Methods for Deep Learning; Theoretic, Practice  Wei Qi Yan Textbook 20211st edition The Editor(s) (if applicable) and The Aut
描述.Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations..Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms..As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learni
出版日期Textbook 20211st edition
關(guān)鍵詞Deep Learning; Machine Learning; Pattern Analysis; Manifold Learning; Machine Vision; Reinforcement Learn
版次1
doihttps://doi.org/10.1007/978-3-030-61081-4
isbn_softcover978-3-030-61083-8
isbn_ebook978-3-030-61081-4Series ISSN 1868-0941 Series E-ISSN 1868-095X
issn_series 1868-0941
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Texts in Computer Sciencehttp://image.papertrans.cn/c/image/232712.jpg
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CapsNet and Manifold Learning, a vector to reflect this relationship. Meanwhile, manifold learning, which is emphasized on infinity continuity?and was originated from differential geometry, has been applied to nonlinear dimensionality reduction?in machine learning.
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https://doi.org/10.1007/978-3-031-35323-9re Embedding) is a deep learning framework, which originally was developed at the University of California, Berkeley. Caffe supports visual object detection and classification as well as image segmentation using CNN, R-CNN, LSTM, and fully connected neural networks. Caffe supports GPU-based and CPU-
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