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Related Questions
- What are vanishing and exploding gradients, and how do they affect the training of deep neural networks?
- How do vanishing gradients impact the selection of optimization algorithms, and what alternatives can be used to mitigate this issue?
- What are the key differences between SGD and Adam in terms of gradient updates, and how do these differences impact convergence rates?
- In what scenarios do exploding gradients occur, and how can optimization algorithms be adapted to handle these situations?
- Can you provide a comparison of the performance of SGD and Adam on tasks with different learning rates, and how do the vanishing and exploding gradient phenomena influence these results?
- How do other optimization algorithms, such as Nesterov Accelerated Gradient (NAG) and RMSProp, address the vanishing and exploding gradient issues, and what are their respective strengths and weaknesses?
- What role does regularization play in mitigating the effects of vanishing and exploding gradients, and how can it be incorporated into optimization algorithms to improve convergence rates?
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