Implement and vectorize NumPy Conv2D

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This is a medium difficulty Data Manipulation (SQL/Python) question, commonly asked during Technical Screen rounds at Tesla.

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This question is commonly asked for Machine Learning Engineer candidates at Tesla during technical interviews.

Question

Implement a 2D convolution operation from scratch using NumPy only (no TensorFlow or PyTorch). Assume NCHW input shape (N, C_in, H_in, W_in) and weights of shape (C_out, C_in, k_h, k_w); support configurable stride and padding. First provide a clear nested-loop reference implementation; then optimize by vectorizing the computation (e.g., im2col or stride tricks) and discuss time/memory trade-offs.

PracHub · Accepted Answer

This question evaluates understanding of 2D convolution mechanics, multidimensional NumPy array manipulation, and the competency to optimize numerical computations for performance and memory.

Quick Overview

Implement and vectorize NumPy Conv2D