The optimizer is an important part of training neural networks. The purpose of using the optimizer does not include which of the following:

Question

Options

• ASpeed up algorithm convergence
• BReduce the difficulty of manual parameter setting
• CAvoid overfitting
• DAvoid local extremes

• A
  8% (3)
• B
  3% (1)
• C
  83% (30)
• D
  6% (2)

Explanation

Optimizers are designed to efficiently minimize a model's loss function, and avoiding overfitting is not part of that role - overfitting is addressed by separate techniques such as regularization (L1/L2), dropout, early stopping, or data augmentation. Option A is wrong as a distractor because optimizers like Adam and RMSprop explicitly accelerate convergence through adaptive learning rates and momentum. Option B is wrong because adaptive optimizers reduce the need to manually tune hyperparameters like the learning rate, which was a major burden with vanilla gradient descent. Option D is wrong because mechanisms like momentum help the optimizer escape local minima and saddle points, making it a genuine optimizer function.

Memory tip: Think of the optimizer as a navigation system that finds the fastest route downhill on a loss landscape - it handles speed (A), autopilot settings (B), and avoiding dead ends (D), but it has no mechanism to prevent the model from memorizing training data, which is the job of regularization tools entirely separate from the optimizer.

No community discussion yet for this question.