Advanced Deep Learning Interview Questions, issue 17, Apr 7, 2026
The Per-Step Update Trap
Senior ML Engineer interview at Google DeepMind, and the interviewer asks:
“You’ve implemented a custom 1D convolutional layer from scratch for specialized edge hardware. During training, the loss plateaus immediately, and the filters completely fail to learn translation invariance. Assuming your forward pass and chain rule math are perfect, what critical gradient aggregation step did you likely forget to apply to the shared weights before updating?”
Applying optimizer steps at each spatial position turns a convolution into an unbounded dense layer and kills both efficiency and generalization.
The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.