Deep Learning CNN Convolutional Neural Networks with Python - Activation Function

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Information Technology (IT), Architecture
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why are activation functions crucial in neural networks?
They introduce non-linearity, allowing complex decision boundaries.
They simplify the network architecture.
They help in linearizing the output.
They reduce the number of neurons required.
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a characteristic of a sigmoid activation function?
It outputs the input value directly.
It outputs 1 for positive inputs and 0 for negative inputs.
It outputs values between 0 and 1, depending on the input size.
It outputs values between -1 and 1.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What happens if a neural network lacks non-linear activation functions?
It requires fewer layers.
It can solve more complex problems.
It becomes more efficient.
It becomes a simple linear regression model.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which activation function is known for its efficiency in computation?
ReLU
Leaky ReLU
Sigmoid
Tanh
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key property of activation functions for effective training?
They should be non-differentiable.
They should be linear.
They should be differentiable.
They should be complex to compute.
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which function is typically used at the output layer for classification problems?
Linear
Tanh
Sigmoid
ReLU
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main advantage of using ReLU over other activation functions?
It is computationally efficient and easy to implement.
It is always linear.
It is non-differentiable.
It outputs negative values for negative inputs.
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