Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
A network with too many layers fails to learn effectively as the gradients become extremely small.
B
A network with very few layers struggles to converge due to excessively large gradients.
C
A network uses dropout to prevent overfitting and improves training speed.
D
A network with batch normalization effectively stabilizes the learning process.
Understanding the Answer
Let's break down why this is correct
Answer
The vanishing/exploding gradients problem happens when the derivatives used to update a network’s weights become extremely small or huge, so learning stalls or becomes unstable. In a deep feed‑forward network, if each layer multiplies the gradient by a number less than one, the gradient shrinks exponentially as it back‑propagates, causing earlier layers to learn almost nothing. Conversely, if each layer multiplies by a number greater than one, the gradient grows exponentially, making weight updates wildly large and the model diverges. For example, a 50‑layer sigmoid network can produce gradients so close to zero that the first few layers never change, illustrating the vanishing gradient issue. This scenario shows the core idea: tiny or huge gradients that prevent effective learning.
Detailed Explanation
When a network has many layers, the small changes that happen in the early layers get multiplied many times. Other options are incorrect because The idea that a few layers cause exploding gradients is a mix‑up; Dropout is a trick to stop a network from memorizing data.
Key Concepts
Vanishing/Exploding Gradients Problem
Deep Neural Networks
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 1How can the vanishing/exploding gradients problem lead to overfitting in business analytics models?
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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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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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Question 4Which 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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5
Question 5Vanishing gradients : shallow networks :: exploding gradients : ?
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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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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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10
Question 10Why do deep neural networks suffer from the vanishing/exploding gradients problem during training?
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