catastrophic forgetting
Applied AI interview questions tagged catastrophic forgetting, across every topic.
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Concepts behind "catastrophic forgetting"
The curriculum that explains the ideas these questions test.
Core
Transfer LearningTransfer learning reuses a model pretrained on a large general corpus as the starting point for a new task, so you inherit learned features instead of training from scratch. The two modes are feature extraction (freeze the backbone, train only a new head) and fine-tuning (unfreeze some layers and keep training), and the choice turns on how much labeled data you have and how far the new domain has drifted. Applied AI interviews probe it because it is the default for vision and NLP when labels are scarce, and because candidates often fine-tune when they should freeze, or vice versa.📊 Evaluation & ML FoundationsSign in
Core
Catastrophic Forgetting and Continual LearningCatastrophic forgetting is when training a neural network on new data erodes capabilities it already had, because gradient updates overwrite the weights that encoded old skills. Applied AI interviews probe it because fine-tuning a model on a narrow task is the most common way teams accidentally break a general model, and knowing the mitigations (data replay, regularization, parameter-efficient methods) separates people who have shipped fine-tunes from people who have only read about them.📊 Evaluation & ML FoundationsSign in