From c6322c68a53c38c26fa2fd0a5898f2afcbbb721b Mon Sep 17 00:00:00 2001 From: Jeremy Howard Date: Fri, 11 Sep 2020 04:07:52 -0700 Subject: [PATCH] multiplication --- 05_pet_breeds.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/05_pet_breeds.ipynb b/05_pet_breeds.ipynb index 57c9321..a6b68ad 100644 --- a/05_pet_breeds.ipynb +++ b/05_pet_breeds.ipynb @@ -1278,7 +1278,7 @@ "\n", " log(a*b) = log(a)+log(b)\n", "\n", - "When we see it in that format, it looks a bit boring; but think about what this really means. It means that logarithms increase linearly when the underlying signal increases exponentially or multiplicatively. This is used, for instance, in the Richter scale of earthquake severity, and the dB scale of noise levels. It's also often used on financial charts, where we want to show compound growth rates more clearly. Computer scientists love using logarithms, because it means that modification, which can create really really large and really really small numbers, can be replaced by addition, which is much less likely to result in scales that are difficult for our computers to handle." + "When we see it in that format, it looks a bit boring; but think about what this really means. It means that logarithms increase linearly when the underlying signal increases exponentially or multiplicatively. This is used, for instance, in the Richter scale of earthquake severity, and the dB scale of noise levels. It's also often used on financial charts, where we want to show compound growth rates more clearly. Computer scientists love using logarithms, because it means that multiplication, which can create really really large and really really small numbers, can be replaced by addition, which is much less likely to result in scales that are difficult for our computers to handle." ] }, {