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0
votes
1answer
33次

为什么一个神经网络更新所有的权重,而不仅仅是第一层的权重

Why are all weights of a neural net updated and not only the weights of the first hidden layer? The error-influence of the prediction by the weights of a neural net is calculated using the chain rule....
0
votes
0答案
44次

Derivation of regularized cost function w.r.t activation and bias

在regularzied成本函数L2正规化的成本增加了。在这里,我们已经计算出交叉熵成本w.r.t $ A,W $。正如正规化笔记本提到的(见下文)...
2
votes
1answer
63点意见

为什么我从Coursera安德鲁吴滑梯反向传播公式不一致的推导?

I am using the cross-entropy cost function to calculate its derivatives using different variables $Z, W$ and $b$ at different instances. Please refer image below for calculation. As per my knowledge, ...
2
votes
1answer
77次点击

What is the neuron-level math behind backpropagation for a neural network?

I am quite new in the AI field. I am trying to create a neural network, in a language (Dart) where I couldn't find examples or premade libraries or tutorials. I've tried looking online for a strictly "...
4
votes
2答案
53 views

我应该读哪线性代数书来了解矢量操作?

我读古德费洛的有关神经网络的书,但我坚持在反向传播算法的数学演算。我理解的原则,与一些YouTube视频说明...
0
votes
1answer
32点意见

反向传播:链式法则到倒数第三层

我试图解决dLoss / DW1。该网络是在下面的图片与身份激活的所有神经元:解决dLoss / DW7简单,因为只有1路输出:$三角洲= OUT-Y $ $损失= ABS(...
4
votes
1answer
148点意见

How are filters weights updated for a CNN?

I've been trying to learn backpropagation for CNNs. I read several articles like this one and this one. They all say that to compute the gradients for the filters, you just do a convolution with the ...
5
votes
2答案
109点意见

是在线反向传播的迭代垂直于约束?

劳尔·罗哈斯神经网络系统地介绍,第8.1.2节涉及离线反向传播和在线反向传播与高斯 - 雅可比和高斯 - 赛德尔方法找到...
3
votes
0答案
607 views

如何计算卷积网络过滤器的梯度

I have similar architecture like in image:CNN. I don't understand how to calculate gradient of filter F. I found these equations(source): Gradient and delta, where first equation calculate gradient ...
1
vote
2答案
3K意见

什么是破RELU激活函数的导数?

I am implementing a feed-forward neural network with leaky ReLU activation functions and back-propagation from scratch. Now, I need to compute the partial derivatives, but I don't know what the ...
9
votes
2答案
2K意见

Is the mean-squared error always convex in the context of neural networks?

Multiple resources I referred to mention that MSE is great because it's convex. But I don't get how, especially in the context of neural networks. Let's say we have the following: $X$: training ...