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About this interview
A technical interview on Convolutional Neural Networks, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Explain convolution, pooling, receptive field, and stride and how they affect spatial dimensions and parameter count
Describe the progression of landmark architectures (LeNet, VGG, ResNet, EfficientNet) and their key innovations
Articulate how skip/residual connections address the degradation problem in deep networks
Discuss object detection paradigms (one-stage vs two-stage) at a conceptual level
Topics covered
Convolution basicsOutput dimension formulaReceptive fieldPoolingParameter efficiencyChannels and filtersStrideArchitecture history - LeNetArchitecture history - VGGReceptive field calculationDegradation problemResidual connectionsResidual connections and gradients1x1 convolutions
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“In a convolutional layer, what are the three properties of the convolution operation that make CNNs fundamentally different from fully connected networks?
“GoogLeNet introduced the Inception module. What problem was the Inception module designed to solve, and what is the key idea behind it?
“In ResNet, why does the identity shortcut work well even when the skip connection bypasses two or more layers? What is the mathematical intuition?