← 📊 Evaluation & ML FoundationsNEXT IN EVALUATION & ML FOUNDATIONSObject Detection and Segmentation→
Core
Catastrophic Forgetting and Continual Learning
Catastrophic 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. AI, ML, and GenAI engineer 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 those who have only read about them.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsWhat is catastrophic forgetting, and how do you prevent it when fine-tuning or continually training an LLM?→LLM & GenAI FundamentalsHow do you keep a deployed LLM current over months without retraining it from scratch?→LLM & GenAI FundamentalsHow do you fine-tune a vision-language model, and what do you freeze?→LLM & GenAI FundamentalsExplain LoRA, QLoRA, and parameter-efficient fine-tuning. Why train a fraction of the parameters?→ML Infrastructure & GPUsHow do you serve many fine-tuned model variants efficiently (multi-LoRA serving)?→LLM & GenAI FundamentalsWhat is QLoRA, and how does it make fine-tuning large models feasible on one GPU?→
COMPANIES THAT ASSUME THIS