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Probably the most insidious problem to encounter is the vanishing gradient. Recall our commonly-used activation functions and their derivatives in Section 4.1.2. For instance, assume that we want to minimize the function f(r) = tanh(x) and we happen to get started at r = 4. As we can see, the gradient of f is close to nil. More specifically, f'(x) = 1- tanh (r) and thus f'(4) = 0.0013. Consequently, optimization will get stuck for a long time before we make progress. This turns out to be one of the reasons that training deep learning models was quite tricky prior to the introduction of the ReLU activation function.

Probably the most insidious problem to encounter is the vanishing gradient. Recall our commonly-used activation functions and their derivatives in Section 4.1.2. For instance, assume that we want to minimize the function f(r) = tanh(x) and we happen to get started at r = 4. As we can see, the gradient of f is close to nil. More specifically, f'(x) = 1- tanh (r) and thus f'(4) = 0.0013. Consequently, optimization will get stuck for a long time before we make progress. This turns out to be one of the reasons that training deep learning models was quite tricky prior to the introduction of the ReLU activation function.

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Probably the most insidious problem to encounter is the vanishing gradient. Recall our commonly-used activation functions and their derivatives in Section 4.1.2. For instance, assume that we want to minimize the function f(x) = tanh(r) and we happen to get started at r = 4. As we can see, the gradient of f is close to nil. More specifically, f'(x) = 1- tanh (x) and thus f'(4) 0.0013. Consequently, optimization will get stuck for a long time before we make progress. This turns out to be one of the reasons that training deep learning models was quite tricky prior to the introduction of the ReLU activation function.
Transcribed Image Text:Probably the most insidious problem to encounter is the vanishing gradient. Recall our commonly-used activation functions and their derivatives in Section 4.1.2. For instance, assume that we want to minimize the function f(x) = tanh(r) and we happen to get started at r = 4. As we can see, the gradient of f is close to nil. More specifically, f'(x) = 1- tanh (x) and thus f'(4) 0.0013. Consequently, optimization will get stuck for a long time before we make progress. This turns out to be one of the reasons that training deep learning models was quite tricky prior to the introduction of the ReLU activation function.
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