PracHub
QuestionsPremiumLearningGuidesCheatsheetNEWCoaches

Quick Overview

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

  • Medium
  • Tesla
  • Data Manipulation (SQL/Python)
  • Machine Learning Engineer

Implement and vectorize NumPy Conv2D

Company: Tesla

Role: Machine Learning Engineer

Category: Data Manipulation (SQL/Python)

Difficulty: Medium

Interview Round: Technical Screen

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.

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

Last updated: Mar 29, 2026

Related Coding Questions

  • Compute nearest index within threshold after walking distances - Tesla (Medium)

Loading coding console...

PracHub

Master your tech interviews with 7,500+ real questions from top companies.

Product

  • Questions
  • Learning Tracks
  • Interview Guides
  • Resources
  • Premium
  • For Universities
  • Student Access

Browse

  • By Company
  • By Role
  • By Category
  • Topic Hubs
  • SQL Questions
  • Compare Platforms
  • Discord Community

Support

  • support@prachub.com
  • (916) 541-4762

Legal

  • Privacy Policy
  • Terms of Service
  • About Us

© 2026 PracHub. All rights reserved.