Implement vectorized NumPy ops and explain broadcasting

Q: Implement vectorized NumPy ops and explain broadcasting

This question evaluates proficiency in vectorized numerical computing with NumPy, understanding of broadcasting semantics, and awareness of numerical stability and algorithmic time/space complexity when manipulating high-dimensional arrays.

Q: How do I approach Data Manipulation (SQL/Python) interview questions?

Data Manipulation (SQL/Python) questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master data manipulation (sql/python) interviews.

Question

Implement vectorized NumPy code for: (a) computing pairwise cosine similarity between two real-valued matrices X (shape n×d) and Y (shape m×d) without explicit Python loops; (b) computing a numerically stable softmax for a 2D array along the last axis; (c) explaining how broadcasting works if X has shape (n, 1, d) and Y has shape (1, m, d). Analyze time and space complexity, and discuss pitfalls such as unintended broadcasting, dtype issues, and memory usage.

Implement vectorized NumPy ops and explain broadcasting

Overview

Comments (0)