Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Using normalized weight initialization
B
Increasing the learning rate
C
Reducing the number of layers
D
Applying dropout after every layer
Understanding the Answer
Let's break down why this is correct
Answer
The most reliable way to keep gradients from dying or blowing up is to use residual connections together with batch‑normalization, as in ResNet. Residual links let the gradient travel directly through identity shortcuts, so it never has to pass through many nonlinear layers that would shrink it. Batch‑normalization keeps the activations in a stable range, preventing large weight updates that would explode the gradient. For example, a ResNet block adds the input to the output of a few convolutional layers, and each layer is followed by batch‑norm and ReLU, so the back‑propagated signal remains strong. This combination consistently keeps training stable even for very deep networks.
Detailed Explanation
When a neural network starts training, the small numbers from each layer can shrink or grow too much. Other options are incorrect because A higher learning rate can make the updates too large, which often makes the gradients explode instead of stabilizing; Removing layers does not solve the root of the problem; the network may still have many weights that can cause vanishing or exploding gradients.
Key Concepts
Vanishing/Exploding Gradients Problem
Neural Network Training Techniques
Residual Learning
Topic
Vanishing/Exploding Gradients Problem
Difficulty
hard 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 strategies most effectively addresses the degradation problem in deep networks while considering training efficiency, optimization techniques, and scalability issues?
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5
Question 5Which of the following scenarios best exemplifies the vanishing/exploding gradients problem in neural networks?
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6
Question 6Which 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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7
Question 7A 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
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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 9Why do deep neural networks suffer from the vanishing/exploding gradients problem during training?
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