Titlebook: Neural Networks and Deep Learning; A Textbook Charu C. Aggarwal Textbook 2023Latest edition Springer Nature Switzerland AG 2023 Neural netw

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書目名稱Neural Networks and Deep Learning影響因子(影響力)




書目名稱Neural Networks and Deep Learning影響因子(影響力)學(xué)科排名




書目名稱Neural Networks and Deep Learning網(wǎng)絡(luò)公開度




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




書目名稱Neural Networks and Deep Learning被引頻次




書目名稱Neural Networks and Deep Learning被引頻次學(xué)科排名




書目名稱Neural Networks and Deep Learning年度引用




書目名稱Neural Networks and Deep Learning年度引用學(xué)科排名




書目名稱Neural Networks and Deep Learning讀者反饋




書目名稱Neural Networks and Deep Learning讀者反饋學(xué)科排名

FUME

Restricted Boltzmann Machines, mapping networks where a set of inputs is mapped to a set of outputs. On the other hand, RBMs are networks in which the probabilistic states of a network are learned for a set of inputs, which is useful for . modeling.

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The Backpropagation Algorithm,This chapter will introduce the backpropagation algorithm, which is the key to learning in multilayer neural networks. In the early years, methods for training multilayer networks were not known, primarily because of the unfamiliarity of the computer science community with ideas that were used quite frequently in control theory [., .].

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Radial Basis Function Networks,Radial basis function (RBF) networks represent a fundamentally different architecture from what we have seen in ?the previous chapters. All the previous chapters use a feed-forward network in which the inputs are transmitted forward from layer to layer in a similar fashion in order to create the final outputs.