From e194ff3d11961341a4ef27b947d286eef3ac7ecb Mon Sep 17 00:00:00 2001 From: Jerod Weinman Date: Thu, 15 Oct 2015 08:48:25 -0500 Subject: [PATCH] Corrected exercise4.m references in Part 3 to exercise3.m --- doc/instructions.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/instructions.md b/doc/instructions.md index 4294d4b..50b5478 100644 --- a/doc/instructions.md +++ b/doc/instructions.md @@ -529,14 +529,14 @@ What you have just witnessed is actually a fairly general principle: centring th Now we will explore several parameters in the algorithms: -> **Task:** Restore the preprocessing as given in `experiment4.m`. Try the following: +> **Task:** Restore the preprocessing as given in `experiment3.m`. Try the following: > > * Try increasing the learning rate `eta`. Can you achieve a better value of the energy in the 500 iterations? > * Disable momentum by setting `momentum = 0`. Now try to beat the result obtained above by choosing `eta`. Can you succeed? Finally, consider the regularisation effect of shrinking: -> **Task:** Restore the learning rate and momentum as given in `experiment4.m`. Then increase the shrinkage factor tenfold and a hundred-fold. +> **Task:** Restore the learning rate and momentum as given in `experiment3.m`. Then increase the shrinkage factor tenfold and a hundred-fold. > > - What is the effect on the convergence speed? > - What is the effect on the final value of the total objective function and of the average loss part of it?