map
AI, ML & GenAI interview questions tagged map, across every topic.
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Concepts behind "map"
The curriculum that explains the ideas these questions test.
Core
MLE, MAP, and Bayesian vs FrequentistMaximum likelihood chooses the parameters that make the observed data most probable; MAP adds a prior and chooses the most probable parameters given the data. MAP reduces to MLE when the prior is flat, and the prior serves as regularization. AI, ML, and GenAI engineer interviews probe this to check whether you know where priors enter your models, why L2 regularization is a Gaussian prior in disguise, and the practical split between point estimates and full posteriors.📊 Evaluation & ML FoundationsSign in
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