题目

英语翻译
Learning in such a network proceeds the same way as for perceptrons:example inputs are presented to the network,and if the network computes an output vector that matches the target,nothing is done.If there is an error (a difference between the output and target),then the weights are adjustec to reduce this error.The trick is to assess the blame for an error and divide it among the contributing weights.In perceptrons,this is easy because there is only one weight between each input and the output.But in multilayer networks.There are many weights connecting each input to an output,and each of these weights contributes to more than one output.
在这样一个网络学习收益,感知器相同的方式：例如输入提交给网络,如果网络计算的输出向量相匹配的目标,不采取任何行动.如果有一个错误（一产出和目标之间的差异）,则权重adjustec减少这种误差.诀窍是评估错误引咎鸿沟在造成重了.在感知,这很容易,因为只有一间每个输入和输出的重量.但在多层网络.有连接每个输入到输出许多重量,而这些重量每有助于多个输出.
The back-propagation algorithm is a sensibly approach to dividing the contribution of each weight.As in the perceptron learning algorithm,we try to minimize th

提问时间：2020-06-12

答案

看到BP了,如果不出所料应该是神经网络的内容不要相信翻译软件,帮你重翻了一遍：术语：weight 权重hidden unit 隐层Learning in such a network proceeds the same way as for perceptrons: example inputs are prese...