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书目名称Guide to Convolutional Neural Networks影响因子(影响力)




书目名称Guide to Convolutional Neural Networks影响因子(影响力)学科排名




书目名称Guide to Convolutional Neural Networks网络公开度




书目名称Guide to Convolutional Neural Networks网络公开度学科排名




书目名称Guide to Convolutional Neural Networks被引频次




书目名称Guide to Convolutional Neural Networks被引频次学科排名




书目名称Guide to Convolutional Neural Networks年度引用




书目名称Guide to Convolutional Neural Networks年度引用学科排名




书目名称Guide to Convolutional Neural Networks读者反馈




书目名称Guide to Convolutional Neural Networks读者反馈学科排名

后退

Convolutional Neural Networks,d some of the libraries that are commonly used for training deep networks. In addition, common metrics (i.e., classification accuracy, confusion matrix, precision, recall, and F1 score) for evaluating classification models were mentioned together with their advantages and disadvantages. Two importan

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Visualizing Neural Networks,ion was regularized using . norm of the image. In the second method, gradient of a particular neuron was computed with respect to the input image and it is illustrated by computing its magnitude. The third method formulated the visualizing problem as an image reconstruction problem. To be more speci

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Small States and the European Migrant Crisis can be used for creating ensemble of models. Then, a method based on optimal subset selection using genetic algorithms were discussed. This way, we create ensembles with minimum number of models that together they increase the classification accuracy. After that, we showed how to interpret and anal

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https://doi.org/10.1007/978-3-642-20766-2ion was regularized using . norm of the image. In the second method, gradient of a particular neuron was computed with respect to the input image and it is illustrated by computing its magnitude. The third method formulated the visualizing problem as an image reconstruction problem. To be more speci

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Traffic Sign Detection and Recognition,work in the field of traffic sign detection and classification is also reviewed. We mentioned several methods based on hand-crafted features and then introduced the idea behind feature learning. Then, we explained some of the works based on convolutional neural networks.

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Caffe Library,lications. In this chapter, we explained how to design and train neural networks using the Caffe library. Moreover, the Python interface of Caffe was discussed using real examples. Then, we mentioned how to develop new layers in Python and use them in neural networks.