regularization-optimization

AI Essentials - Grok · 24 questions

• When comparing L1 and L2 weight regularization, which statement correctly describes a key difference?
• In gradient-based training, what does the learning rate primarily control?
• A model scores nearly perfectly on the training set but performs poorly on held-out validation and test examples. Which phenomenon best describes this behavior?
• What is Bayesian optimization primarily used for in machine learning workflows?
• During training of a deep network, what is the main purpose of a dropout layer?
• After deployment, when should overfitting be treated as a serious problem for an AI model?
• A team is tuning a gradient-boosted classifier and wants a systematic way to try every combination of learning rate, tree depth, and subsample ratio from a predefined set. Which technique is designed for that exhaustive hyperparameter exploration?
• In machine learning workflows, what is a hyperparameter?
• Which description correctly defines model overfitting?
• What is the purpose of batch gradient descent when training a model?
• A data scientist trains a decision tree on a customer churn dataset and notices nearly perfect accuracy on the training rows but poor results on a holdout set. Which description best captures overfitting for this tree?
• After training a logistic regression classifier, you observe much higher metrics on the training set than on a held-out test set. What is the most likely explanation?
• In model development, what are grid search and random search primarily used for?
• Compared with classic stochastic gradient descent (SGD), what is the primary practical advantage of the Adam optimizer?
• When training a neural network, what is the main role of the Adam optimizer regarding parameter updates?
• Compared with plain stochastic gradient descent (SGD), what is the primary benefit of using RMSProp as an optimizer?
• In deep learning, what does hyperparameter tuning mean?
• A training team notices their deep network memorizes the training set and fails to generalize. They enable weight decay (L2 regularization). What is the primary purpose of this technique?
• Why do practitioners insert a dropout layer while training a neural network?
• In deep learning optimization, what does the learning rate control?
• What primary role does batch normalization play when inserted into a deep neural network training pipeline?
• What is a key distinction between stochastic gradient descent (SGD) and the Adam optimizer?
• A team reports excellent accuracy on the training set, yet the same model performs poorly on new, previously unseen examples. What is the most likely diagnosis and a sensible first-line remedy?
• Your practice results show a clear weak spot on deep learning optimizers (for example SGD, Adam, and learning-rate schedules). What is the most effective way to improve before the certification exam?