Training method and device of neural network model for sample classification
A neural network model and sample technology, applied in the field of neural network model training, can solve the problem of high complexity of neural network models, and achieve the effect of preventing overfitting and improving generalization.
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[0036] The solutions provided in this specification will be described below in conjunction with the accompanying drawings.
[0037] figure 1 It is a schematic diagram of an implementation scenario of an embodiment disclosed in this specification. This implementation scenario involves the training of a neural network model for sample classification. Specifically, the neural network model may be trained based on the training samples. In the embodiment of the present specification, the training samples have sample identifiers and pre-marked sample category labels. It can be understood that the above neural network model can be applied to classify samples in various scenarios, for example, classify items, classify users, and so on. In an example, the training sample corresponds to one user, the sample identifier is an identifier of the one user, and the sample category label corresponds to a user group including multiple users. refer to figure 1 , each training sample included...
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