map
Applied AI interview questions tagged map, across every topic.
3 questions · 1 unlocked for you
Concepts behind "map"
The curriculum that explains the ideas these questions test.
Core
MLE, MAP, and Bayesian vs FrequentistMaximum likelihood picks the parameters that make the observed data most probable; MAP adds a prior and picks the most probable parameters given the data. MAP reduces to MLE when the prior is flat, and the prior acts as regularization. Applied-AI interviews probe this to see if you understand 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 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