3.2 CNNs (conceptual)
Interview-ready field notes
The 20-second answer
CNNs learn small filters that slide over an image, recognizing local patterns while reusing the same weights everywhere.
Core ideas to retain
- A convolutional filter produces a feature map: early filters find edges, later layers combine them into shapes and objects.
- Weight sharing cuts parameters and gives translation equivariance: moving an object moves its activation map.
- Stride controls how far the filter moves; padding preserves borders or output size; pooling/downsampling trades detail for scale and efficiency.
- A receptive field is the region of the input that can influence one activation; it grows with depth.
Interview / OA rule
Output size per spatial dimension is . State the assumptions about dilation if it appears in an OA question.
One good written resource
CS231n — Convolutional Neural Networks — read this after the video when you want a clearer mental model, not more pages of notes.
Most asked
Interview questions to practise aloud
Each answer is the level of detail expected for a strong fundamentals round.
Why are CNNs parameter-efficient?
The same small kernel is reused at every spatial location, unlike a dense layer that needs separate weights for every pixel-to-output connection.
What do stride and padding do?
Stride downsamples by skipping positions; padding adds border values so edge information is kept and spatial size can be controlled.
Convolution vs pooling?
Convolution learns features with weights. Pooling is usually a fixed local aggregation that reduces spatial resolution.