From 152b1d7d52a45796a9303c15df8cb59b8b70bfca Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Thu, 29 Sep 2022 07:58:09 +0200 Subject: [PATCH] typo --- doc/pub/week39/html/week39-reveal.html | 3 +-- doc/pub/week39/html/week39-solarized.html | 3 +-- doc/pub/week39/html/week39.html | 3 +-- doc/pub/week39/ipynb/ipynb-week39-src.tar.gz | Bin 193 -> 193 bytes doc/src/week39/week39.do.txt | 3 +-- 5 files changed, 4 insertions(+), 8 deletions(-) diff --git a/doc/pub/week39/html/week39-reveal.html b/doc/pub/week39/html/week39-reveal.html index 9c2d33cc5..22236b38e 100644 --- a/doc/pub/week39/html/week39-reveal.html +++ b/doc/pub/week39/html/week39-reveal.html @@ -1809,8 +1809,7 @@ training data can have on order of millions of examples. Hence, it seems wasteful to compute the full cost function over the entire training set in order to perform only a single parameter update. A very common approach to addressing this challenge is to compute the -gradient over batches of the training data. For example, in current -a typical batch could contain some thousand examples from +gradient over batches of the training data. For example, a typical batch could contain some thousand examples from an entire training set of several millions. This batch is then used to perform a parameter update.

diff --git a/doc/pub/week39/html/week39-solarized.html b/doc/pub/week39/html/week39-solarized.html index 519b915b5..a503ae23e 100644 --- a/doc/pub/week39/html/week39-solarized.html +++ b/doc/pub/week39/html/week39-solarized.html @@ -1774,8 +1774,7 @@ training data can have on order of millions of examples. Hence, it seems wasteful to compute the full cost function over the entire training set in order to perform only a single parameter update. A very common approach to addressing this challenge is to compute the -gradient over batches of the training data. For example, in current -a typical batch could contain some thousand examples from +gradient over batches of the training data. For example, a typical batch could contain some thousand examples from an entire training set of several millions. This batch is then used to perform a parameter update.

diff --git a/doc/pub/week39/html/week39.html b/doc/pub/week39/html/week39.html index 2240c704a..6da4ddd96 100644 --- a/doc/pub/week39/html/week39.html +++ b/doc/pub/week39/html/week39.html @@ -1851,8 +1851,7 @@ training data can have on order of millions of examples. Hence, it seems wasteful to compute the full cost function over the entire training set in order to perform only a single parameter update. A very common approach to addressing this challenge is to compute the -gradient over batches of the training data. For example, in current -a typical batch could contain some thousand examples from +gradient over batches of the training data. For example, a typical batch could contain some thousand examples from an entire training set of several millions. This batch is then used to perform a parameter update.

diff --git a/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz b/doc/pub/week39/ipynb/ipynb-week39-src.tar.gz index da718dc89fdd058889201a61865af3275cf4ddb6..43891e1b533edb3e3fa421c7f00021740c5e162d 100644 GIT binary patch literal 193 zcmV;y06za8iwFSRGc{uX1MSaC3c@fD2H>uHia9|^nxw_0U>7a~5igL^)W+JRCMnw6 z+Xv`MaZ^OdxA_@n7-kOHdb7(ScXz>J5JCy1FlL&tDN#Jv6O0*PN-&EFP6-8s35$~e z&~hiebk=b*oYGWhp{!8v=7zDd{IF+!1)ljQj+L~q-S@823Y2yt*SdzA5H(pvvh7t4 vg%);fff3hE8UbAOz>7jUsTIG3tZ~bRYDH00;m84uHia9|^nm%wT*o6y0#0#W!YGZ9ulN9ak z?E`eBxG5s!+x!eO3^Rvpz1d}fy<2ZFgpeePV9Yd$rzGKAPbiInMvTTGOxz&p3|bIA41JZ(m#o00;m8tgu%| diff --git a/doc/src/week39/week39.do.txt b/doc/src/week39/week39.do.txt index c703f04bd..cf9948925 100644 --- a/doc/src/week39/week39.do.txt +++ b/doc/src/week39/week39.do.txt @@ -1217,8 +1217,7 @@ training data can have on order of millions of examples. Hence, it seems wasteful to compute the full cost function over the entire training set in order to perform only a single parameter update. A very common approach to addressing this challenge is to compute the -gradient over batches of the training data. For example, in current -a typical batch could contain some thousand examples from +gradient over batches of the training data. For example, a typical batch could contain some thousand examples from an entire training set of several millions. This batch is then used to perform a parameter update.