Artificial Intelligence Neural Networks and Deep Learning 1

Question 1

What is the activation function that outputs values strictly between 0 and 1, commonly used in binary classification output layers?

Accepted Answer

Sigmoid

Answer

The sigmoid function squashes any real-valued input to the (0, 1) range, making it useful for binary probability outputs.

Question 2

Which layer type in a convolutional neural network applies a learned filter to detect local patterns in an image?

Accepted Answer

Convolutional layer

Answer

A convolutional layer slides learned filters over the input to produce feature maps capturing local spatial patterns like edges and textures.

Question 3

What problem does the 'vanishing gradient' phenomenon cause in deep neural networks?

Accepted Answer

Gradients become very small in early layers, halting learning

Answer

Vanishing gradients occur when backpropagated error signals shrink exponentially through many layers, making early weights nearly untrainable.

Question 4

What is the purpose of a dropout layer in a neural network?

Accepted Answer

To randomly deactivate neurons during training to reduce overfitting

Answer

Dropout randomly sets a fraction of neuron activations to zero during each training step, acting as an ensemble regularization technique.

Question 5

Which optimizer adapts the learning rate for each parameter based on historical gradient information?

Accepted Answer

Adam

Answer

Adam (Adaptive Moment Estimation) maintains per-parameter adaptive learning rates using estimates of first and second moments of gradients.

Artificial Intelligence Practice Test