Titlebook: Deep Learning Architectures; A Mathematical Appro Ovidiu Calin Textbook 2020 Springer Nature Switzerland AG 2020 neural networks.deep learn

intern

书目名称Deep Learning Architectures影响因子(影响力)




书目名称Deep Learning Architectures影响因子(影响力)学科排名




书目名称Deep Learning Architectures网络公开度




书目名称Deep Learning Architectures网络公开度学科排名




书目名称Deep Learning Architectures被引频次




书目名称Deep Learning Architectures被引频次学科排名




书目名称Deep Learning Architectures年度引用




书目名称Deep Learning Architectures年度引用学科排名




书目名称Deep Learning Architectures读者反馈




书目名称Deep Learning Architectures读者反馈学科排名

micronized

Cost Functionsity between the prediction of the network and the associated target. This is also known under the equivalent names of ., ., or .. In the following we shall describe some of the most familiar cost functions used in neural networks.

疯狂

Allege

Neural Networksyers of neurons, forming .. A layer of neurons is a processing step into a neural network and can be of different types, depending on the weights and activation function used in its neurons (fully-connected layer, convolution layer, pooling layer, etc.) The main part of this chapter will deal with t

同时发生

Approximation Theoremsapproximation results included in this chapter contain Dini’s theorem, Arzela-Ascoli’s theorem, Stone-Weierstrass theorem, Wiener’s Tauberian theorem, and the contraction principle. Some of their applications to learning will be provided within this chapter, while others will be given in later chapt

moribund

moribund

Information Representationnd networks using the concept of sigma-algebra. The main idea is to describe the evolution of the information content through the layers of a network. The network’s input is considered to be a random variable, being characterized by a certain information. Consequently, all network layer activations

Obvious

2365-5674 ates.  In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.. . . .978-3-030-36723-7978-3-030-36721-3Series ISSN 2365-5674 Series E-ISSN 2365-5682

Consensus

Textbook 2020 universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegan

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