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Question at position 19 How does the learning rate impact the performance of gradient descent in training neural networks?A low learning rate guarantees perfect convergence without any issues. A high learning rate ensures faster convergence but may cause overshooting and instability. A fixed learning rate works optimally for all types of neural networks.The learning rate has no effect on gradient descent as long as backpropagation is used.

选项
A.A low learning rate guarantees perfect convergence without any issues.
B.A high learning rate ensures faster convergence but may cause overshooting and instability.
C.A fixed learning rate works optimally for all types of neural networks.
D.The learning rate has no effect on gradient descent as long as backpropagation is used.
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思路分析
To understand how learning rate affects gradient descent, let's evaluate each option with nuanced reasoning. Option 1: 'A low learning rate guarantees perfect convergence without any issues.' While a small learning rate can lead to more precise convergence and reduce overshooting, it does not guara......Login to view full explanation

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类似问题

Which of the following statements about gradient descent and learning rate is true?

Which statement is correct?  

假设你正在训练一个网络，参数为 [4.5, 2.5, 1.2, 0.6]，学习率为 0.2，梯度为 [-1, 9, 2, 5]。更新一个梯度下降步长后，网络的参数等于多少？ Suppose that you are training a network with parameters [4.5, 2.5, 1.2, 0.6], a learning rate of 0.2, and a gradient of [-1, 9, 2, 5]. After one update step of gradient descent, what would your network's parameters be equal to?

在梯度下降中如何更新参数？How do we update the parameters in gradient descent?

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