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

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書目名稱Deep Learning Architectures影響因子(影響力)




書目名稱Deep Learning Architectures影響因子(影響力)學(xué)科排名




書目名稱Deep Learning Architectures網(wǎng)絡(luò)公開度




書目名稱Deep Learning Architectures網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Deep Learning Architectures被引頻次




書目名稱Deep Learning Architectures被引頻次學(xué)科排名




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書目名稱Deep Learning Architectures年度引用學(xué)科排名




書目名稱Deep Learning Architectures讀者反饋




書目名稱Deep Learning Architectures讀者反饋學(xué)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.

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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

同時發(fā)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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