From fbf414a0407e492371c736c895257b2072962934 Mon Sep 17 00:00:00 2001 From: Mitja Felicijan Date: Tue, 25 Sep 2018 11:49:54 +0200 Subject: content update --- slides/presentations/basic-math-in-programming/default.pug | 5 ----- 1 file changed, 5 deletions(-) (limited to 'slides/presentations/basic-math-in-programming') diff --git a/slides/presentations/basic-math-in-programming/default.pug b/slides/presentations/basic-math-in-programming/default.pug index 41792ee..7ca262b 100644 --- a/slides/presentations/basic-math-in-programming/default.pug +++ b/slides/presentations/basic-math-in-programming/default.pug @@ -15,8 +15,6 @@ section.center q We Cannot Solve Our Problems With The Same Thinking We Used When We Created Them. footer — Albert Einstein - - section h2 How we usually find solutions and why this is problematic? @@ -26,7 +24,6 @@ section li We don't take enough time to properly understand problem we a re trying to solve. li Brute force solutions we make are usually not optimized - section h2 Levenshtein distance p The Levenshtein distance is a string metric for measuring difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (i.e. insertions, deletions or substitutions) required to change one word into the other. @@ -194,8 +191,6 @@ section | background: black; | } - - section h3 Grid example div.grid.col-1-1 -- cgit v1.2.3