Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
deep networks
B
wide networks
C
normalized networks
D
recurrent networks
Understanding the Answer
Let's break down why this is correct
Answer
Vanishing gradients are a problem that mainly shows up in shallow networks, where the signal from the output layer fades as it travels back through the layers. In the same way, exploding gradients are a problem that mainly shows up in deep networks, where the signal grows uncontrollably as it propagates backward. Both issues arise when the product of many weight matrices multiplies a gradient, causing it to shrink or blow up. For example, if a deep network has many layers with weights slightly larger than one, the gradient can grow exponentially, making training unstable. Thus, the analogy is Vanishing gradients : shallow networks :: Exploding gradients : deep networks.
Detailed Explanation
When a network has many layers, the gradient can become very small or very large as it moves backward. Other options are incorrect because A wider network means more neurons per layer, but that does not change how gradients multiply across layers; Normalization techniques, like batch norm, actually help keep gradients in a safe range.
Key Concepts
Vanishing Gradients
Exploding Gradients
Deep Learning
Topic
Vanishing/Exploding Gradients Problem
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1Which of the following scenarios best exemplifies the vanishing/exploding gradients problem in neural networks?
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Practice
2
Question 2Which of the following techniques can help mitigate the vanishing or exploding gradients problem in deep neural networks? Select all that apply.
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Practice
3
Question 3A team of researchers is developing a deep neural network for image recognition, but they notice that the network struggles to learn effectively as they increase the number of layers. Which of the following strategies would best address the vanishing/exploding gradients problem they are facing?
mediumComputer-science
Practice
4
Question 4Why do deep neural networks suffer from the vanishing/exploding gradients problem during training?
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Practice
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