transfer learning
Applied AI 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.
Core
CV Architectures: ResNets, ViT, DetectionModern computer vision rests on three pillars: residual connections that let CNNs go hundreds of layers deep without degrading, Vision Transformers that patchify an image and run self-attention instead of convolutions, and detection heads (one-stage vs two-stage) scored by mAP after non-maximum suppression. Applied AI interviews probe this to check 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 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