WorksheetsWeek2_S2
Total questions: 10
Worksheet time: 8mins
Why is generalization more important than training accuracy in neural networks?
Generalization proves convergence.
Training accuracy ensures bias reduction.
It prevents vanishing gradients.
It reflects the ability to predict unseen data, the true goal.
A multilayer network without nonlinearities collapses into a (a) model.
When using ReLU in hidden layers instead of sigmoid, which benefit typically emerges?
Guarantees linear separability in the input space.
Prevents exploding gradients.
Ensures all neurons remain active.
Reduces vanishing gradient problems
Which of the following statements about gradient descent is correct?
It always finds the global minimum.
It updates weights by moving against the gradient of the loss function.
It requires linear separability.
It is identical to the perceptron update rule.
Why is nonlinearity essential in deep neural networks?
It guarantees zero error.
It reduces training time.
It allows the composition of layers to model complex, non-linear boundaries.
It simplifies the optimization problem.
Backpropagation uses the (a) rule to propagate gradients backward through layers
In a neural network, forward propagation refers to:
Feeding inputs through the network to generate predictions
Updating weights using gradient descent
Reversing gradients to find errors
Adjusting biases to prevent saturation
Which activation function is most commonly used in modern deep networks for hidden layers?
Sigmoid
Tanh
ReLU
Sign function
A perceptron without a bias term may struggle because:
It cannot represent decision boundaries that don't pass through the origin
It cannot compute loss correctly
It requires too many hidden layers
It cannot apply nonlinear activations
Which statement about cross-entropy loss is correct?
It measures the squared difference between predicted and actual values
It is used to calculate the probability of linear separability
It penalizes wrong predictions by increasing loss logarithmically
It works only with ReLU activations
