Neural Network Initialization Quiz
Test your knowledge of neural network initialization techniques and best practices.
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Which of the following is a purpose of weight initialization in neural networks?
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To avoid vanishing or exploding gradients
To speed up convergence during training
To ensure reproducibility
All of the above
Select all initialization methods commonly used for neural networks:
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Xavier (Glorot) Initialization
He Initialization
Zero Initialization
Random Normal Initialization
Uniform Initialization
Other
Match the initialization method to its recommended activation function:
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Rows
ReLU
Tanh
Sigmoid
Activation Function
He Initialization
Xavier Initialization
Random Uniform
He Initialization
Xavier Initialization
Random Uniform
He Initialization
Xavier Initialization
Random Uniform
Rate your familiarity with the impact of initialization on training speed:
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Not familiar
1
2
3
4
Very familiar
5
1 is Not familiar, 5 is Very familiar
Which problems can occur if all weights are initialized to zero?
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Symmetry breaking fails, leading to poor learning
Network converges faster
No effect on training
Other
Which of the following statements about Xavier initialization is correct?
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It is designed for layers with sigmoid or tanh activations
It is only used for output layers
It is optimal for ReLU activations
None of the above
Why is random initialization preferred over constant initialization?
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Suppose you observe vanishing gradients during training. Which initialization technique might help and why?
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Please provide an example scenario where He initialization is preferable.
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