backbone
AI, ML & GenAI interview questions tagged backbone, across every topic.
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Concepts behind "backbone"
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 rather than 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 hinges on how much labeled data you have and how far the new domain has drifted. AI, ML, and GenAI engineer 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 the reverse.📊 Evaluation & ML FoundationsSign in
Core
The Computer Vision PipelineA production CV system is a chain: ingest and version images, preprocess and augment, fine-tune a pretrained backbone, attach a task head, evaluate with sliced metrics, post-process, then serve and monitor. The invariant that divides working systems from broken ones is train/serve consistency: the exact resize, color space, and normalization have to match at training and inference. AI, ML, and GenAI engineer interviews probe this because most CV failures live at the preprocessing seam, not in the architecture.📊 Evaluation & ML FoundationsSign in