TensorFlow Model Training and Optimization 1

Question 1

Which optimizer uses adaptive learning rates based on first and second moment estimates?

Accepted Answer

Adam

Answer

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

Question 2

What does the learning rate hyperparameter control in gradient descent?

Accepted Answer

The step size taken toward the minimum of the loss function

Answer

The learning rate determines how large a step is taken in the direction of the negative gradient during each parameter update.

Question 3

What is gradient clipping used for in TensorFlow training?

Accepted Answer

Preventing exploding gradients by capping gradient magnitude

Answer

Gradient clipping limits gradient values to a maximum norm or value, preventing the exploding gradient problem in deep or recurrent networks.

Question 4

Which TensorFlow callback reduces the learning rate when a metric plateaus?

Accepted Answer

ReduceLROnPlateau

Answer

ReduceLROnPlateau monitors a metric and reduces the learning rate by a factor when improvement stalls for a specified number of epochs.

Question 5

What is the vanishing gradient problem in deep neural networks?

Accepted Answer

Gradients become extremely small during backpropagation, preventing weight updates in early layers

Answer

Vanishing gradients occur when gradients shrink exponentially as they propagate backward through many layers, making early layers train very slowly.

TensorFlow Practice Test