[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fzX4c7Mm5ih9MyT5ivvkVCvX8Qkb6YEytmY8M5mvzpYk":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":18,"related":19,"source":29,"type":30},[],"2023-05-08 17:54:43",84708368,[8,9,10,11],"神经元节点先计算激活函数,再计算线性函数(z = Wx + b)","神经元节点先计算线性函数(z = Wx + b),再计算激活","神经元节点计算函数g,函数g计算(Wx + b)","在将输出应用于激活函数之前,神经元节点计算所有特征的平均值",{"courseId":13,"courseImg":14,"courseName":15,"workId":16,"workName":17},"1000009913","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F2ecd5d006a11b86c74ea363647488dfc.jpg","数据挖掘","29404969","第七章单元测试","神经元节点计算什么()",[20,31,40,48,51],{"answer":21,"createTime":5,"id":22,"options":23,"question":28,"source":29,"type":30},[],84708345,[24,25,26,27],"4, 3, 1, 5, 2","3, 2, 1, 5, 4","5, 4, 3, 2, 1","1, 2, 3, 4, 5","梯度下降算法的正确步骤是什么?1.计算预测值和真实值之间的误差 2.迭代跟新,直到找到最佳权重3.把输入传入网络,得到输出值4.初始化随机权重和偏差5.对每一个产生误差的神经元,改变相应的(权重)值以减小误差","v2",0,{"answer":32,"createTime":5,"id":33,"options":34,"question":39,"source":29,"type":30},[],84708348,[35,36,37,38],"加入更多层,使神经网络的深度增加","有维度更高的数据","当这是一个图形识别的问题时","都不正确","以下什么情况下神经网络模型被称为深度学习模型",{"answer":41,"createTime":5,"id":42,"options":43,"question":46,"source":29,"type":47},[],84708349,[44,45],"对","错","卷积神经网络可以对一个输入完成不同种类的变换(旋转或缩放),这个表述正确吗",3,{"answer":49,"createTime":5,"id":6,"options":50,"question":18,"source":29,"type":30},[],[8,9,10,11],{"answer":52,"createTime":5,"id":53,"options":54,"question":58,"source":29,"type":30},[],84708369,[55,56,57,38],"随机赋值,祈祷它们是正确的","搜索所有权重和偏差的组合,直到得到最佳值","赋予一个初始值,通过检查跟最佳值的差值,然后迭代更新权重","在一个神经网络里,知道每一个神经元的权重和偏差是最重要的一步.如果以某种方法知道了神经元准确的权重和偏差,你就可以近似任何函数.实现这个最佳的办法是什么"]