From 75c1b5dcc1140b840236d50121eccfa4e64aa28d Mon Sep 17 00:00:00 2001 From: kanungle Date: Tue, 17 Mar 2026 05:50:45 -0700 Subject: [PATCH] grammar formatting --- .../content/course/essentials/day-3/sparse-vectors.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/qdrant-landing/content/course/essentials/day-3/sparse-vectors.md b/qdrant-landing/content/course/essentials/day-3/sparse-vectors.md index 05c502a6c..7b653a47c 100644 --- a/qdrant-landing/content/course/essentials/day-3/sparse-vectors.md +++ b/qdrant-landing/content/course/essentials/day-3/sparse-vectors.md @@ -45,7 +45,7 @@ User_2: [ 0, 0, 0, 0, 4, 0, 0, 2, 0, 0 ] Comparing two sparse representations, you'd be usually interested in how much they agree on the same features/objects (e.g., the same movie rating). -The **dot product** distance metric, introduced in the **day 1** (*Vector Search Fundamentals/Distance Metrics*), is a perfect fit for measuring the similarity between sparse representations. +The **dot product** distance metric, introduced in the **Day 1** (*Vector Search Fundamentals/Distance Metrics*), is a perfect fit for measuring the similarity between sparse representations. It multiplies corresponding dimensions and summes the results. ```text