Research on Intelligent Customization of Cross-Border E-Commerce Based on Deep Learning

Table 2

Deep learning solution algorithm.

Deep learning solution algorithm
Input	Input X to represent the characteristic attributes
	Input Y for the true value of the tag attributes
Defining hyperparameters	Training round epoch
	Learning rate
Initializing weights and biases	Weight and bias
Defining loss function
Training neural network	For i in range(epoch):
	Feedforward training, transferring the sample data X to the neural network
	Feedback training: we update the weight deviation to minimize the loss function
	Where X is the input, H is the output of the hidden layer, is the weight, is the activation function, is the predicted value of the output, and Y is the true value