18 Questions
What does a large value for the derivative indicate during training?
The weights are far from a minimum
Why is it not recommended to use a linear activation function in neural networks?
It collapses all layers into one
What happens to a neural network if all activation functions used are linear?
The network collapses to one layer
Which of the following is true about the linear activation function?
It turns the neural network into just one layer
Why do most modern neural networks prefer non-linear activation functions over linear ones?
To enable complex transformations
What is the main disadvantage of a linear activation function concerning backpropagation?
It prevents backpropagation entirely
What is the main purpose of using activation functions in artificial neural networks?
To decide whether a neuron can be activated or not
Why is the derivative of an activation function important in training a neural network?
It indicates the function's sensitivity to change with respect to its input
Which of the following is a key benefit of using non-linear activation functions in neural networks?
They help the network learn high-order polynomials
What is the main purpose of the training process in a neural network?
To minimize the squared differences between observed and predicted data
How does the derivative of an activation function affect the training of a neural network?
It determines the speed of convergence during training
Which of the following is a key difference between linear and non-linear activation functions in neural networks?
Linear activation functions can model high-order polynomials
Why is it important for an activation function to have a smooth gradient?
To prevent jumps in output values during training
What is the derivative of the sigmoid activation function?
$sigmoid(x) * (1 - sigmoid(x))$
What is a limitation of the sigmoid activation function?
Both (a) and (b)
What is the key difference between the sigmoid and tanh activation functions?
The output range of the tanh function is -1 to 1, while the sigmoid function's output range is 0 to 1
Which type of activation function is the linear activation function?
Linear activation function
Which type of activation function are the sigmoid and tanh functions?
Non-linear activation functions
Test your knowledge on types of activation functions in the context of data visualization. Learn about linear and non-linear activation functions, their role in adjusting weights during optimization, and the concept of steepest descent surface.
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