Reinforcement Learning and Deep RL Python Theory and Projects - DNN Properties of Activation Function

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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is nonlinearity important in neurons of a neural network?
To simplify the network architecture
To reduce the number of neurons needed
To make the network faster
To ensure the network can approximate complex functions
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a common practice regarding activation functions in neural networks?
Avoiding activation functions in hidden layers
Changing activation functions dynamically during training
Applying a single activation function throughout the network
Using a different activation function for each neuron
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which activation function is known for its simplicity and efficiency?
Tanh
Softmax
Sigmoid
ReLU
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a key property of the Sigmoid activation function?
It outputs values between -1 and 1
It is linear for all input values
It outputs values between 0 and 1
It is non-differentiable
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which activation function is typically used in the output layer for classification tasks?
Linear
Tanh
ReLU
Sigmoid
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a crucial property of activation functions for learning parameters in neural networks?
They must be non-invertible
They should be linear
They should be easy to compute
They must be non-differentiable
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is differentiability important for activation functions?
To make the network faster
To ensure the network can be trained using gradient descent
To reduce the number of neurons needed
To simplify the network architecture
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