Learning Path
Question & Answer1
Understand Question2
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Explore TopicChoose the Best Answer
A
It ensures that all neurons start with the same weights, leading to uniform training.
B
It helps maintain the scale of the gradients throughout the layers, preventing them from becoming too small or too large.
C
It guarantees that the learning rate will always be optimal, which prevents gradient issues.
D
It automatically adjusts the architecture of the network to prevent gradient problems.
Understanding the Answer
Let's break down why this is correct
Answer
Proper weight initialization keeps the signal flowing through the layers so that the gradients neither shrink to zero nor blow up during backpropagation. By choosing a variance that matches the layer’s fan‑in and fan‑out, the forward activations maintain a roughly constant scale and the backward gradients stay at a comparable magnitude. For example, initializing ReLU layers with He initialization (variance = 2/fan‑in) ensures that each neuron’s output variance stays near one, so the gradient for each layer stays close to the input gradient’s size. If the weights were too small, each successive layer would dampen the gradient, leading to vanishing gradients; if too large, the gradient would grow exponentially, causing exploding gradients. Thus, a careful initialization prevents these extremes and allows deep networks to learn effectively.
Detailed Explanation
When weights are chosen with the right size, each layer passes gradients that stay about the same size. Other options are incorrect because People think giving every neuron the same weight will make training smooth, but it keeps all neurons identical; Weight initialization does not set the learning rate.
Key Concepts
backpropagation
weight initialization
Topic
Vanishing/Exploding Gradients Problem
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In 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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2
Question 2In 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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3
Question 3Which 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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4
Question 4A 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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5
Question 5Arrange 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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6
Question 6In the context of deep learning, which method is most effective in mitigating the vanishing/exploding gradients problem during training?
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7
Question 7Why do deep neural networks suffer from the vanishing/exploding gradients problem during training?
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