transfer learning
AI, ML & GenAI interview questions tagged transfer learning, across every topic.
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Concepts behind "transfer learning"
The curriculum that explains the ideas these questions test.
Foundational
Foundation Models and the Pretrain-Adapt ParadigmA foundation model is a single large model pretrained on broad data and then adapted to many tasks, replacing the old habit of training one bespoke model per task. The scarce resource moved: it used to be labeled data and training compute, and now it is evaluation and context. AI, ML, and GenAI engineer interviews probe this because it explains why a prototype takes an afternoon while a reliable product still takes a quarter, and because knowing when a gradient-boosted tree still beats an LLM is a senior signal.🧠 Foundations of LLMs & GenAI
Core
CV Architectures: ResNets, ViT, DetectionModern computer vision stands on three pillars: residual connections that let CNNs reach hundreds of layers deep without degrading, Vision Transformers that patchify an image and run self-attention in place of convolutions, and detection heads (one-stage vs two-stage) scored by mAP after non-maximum suppression. AI, ML, and GenAI engineer interviews probe this to check that you can pick an architecture, fine-tune a pretrained backbone, and reason about latency vs accuracy rather than train from scratch.📊 Evaluation & ML FoundationsSign in
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