Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Because the gradients become too small or too large, affecting weight updates
B
Because deeper networks have more parameters leading to overfitting
C
Because shallow networks are easier to optimize
D
Because activation functions are always linear in deep networks
Understanding the Answer
Let's break down why this is correct
Answer
During back‑propagation, the gradient of the loss with respect to each weight is computed by multiplying partial derivatives along each layer; if each derivative is slightly less than one, repeated multiplication shrinks the gradient toward zero, a phenomenon called vanishing gradients, while if each derivative is slightly greater than one the product explodes, blowing up the gradient. This happens especially in deep nets where many layers stack, and activation functions like sigmoid or tanh saturate so their derivatives are small, amplifying the shrinkage. For example, in a 10‑layer network each layer’s derivative might be 0. 5, so the gradient after 10 layers becomes 0. 5¹⁰ ≈ 0.
Detailed Explanation
When a gradient moves backward through many layers, it can shrink to almost nothing or grow to a huge number. Other options are incorrect because The idea that more parameters automatically cause vanishing or exploding gradients is a mix‑up with overfitting; Thinking that shallow networks are always easier to train ignores the fact that any network can have bad gradient flow.
Key Concepts
Vanishing/Exploding Gradients Problem
Deep Neural Networks
Weight Optimization Techniques
Topic
Vanishing/Exploding Gradients Problem
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of training deep neural networks, how does proper weight initialization help mitigate the vanishing/exploding gradients problem during backpropagation?
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2
Question 2In the context of training deep neural networks, which of the following scenarios best illustrates the impact of the vanishing/exploding gradients problem on backpropagation, training stability, and the risk of overfitting?
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3
Question 3In the context of deep learning architectures, how can proper weight initialization and gradient clipping address the vanishing/exploding gradients problem effectively?
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4
Question 4Which of the following scenarios best exemplifies the vanishing/exploding gradients problem in neural networks?
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5
Question 5Which 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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6
Question 6A 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?
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7
Question 7What is a primary cause of the vanishing gradients problem in deep neural networks?
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8
Question 8Arrange the following steps in addressing the vanishing/exploding gradients problem in deep neural networks from first to last: A) Implement normalization techniques, B) Train the network, C) Initialize weights appropriately, D) Monitor gradient behavior during training.
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9
Question 9In the context of deep learning, which method is most effective in mitigating the vanishing/exploding gradients problem during training?
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