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* fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * Update practicum05.sbv * fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * fix unicode * fix timers * tweaking * Update lecture01.sbv * Update practicum01.sbv * Create lecture02.sbv * Create practicum02.sbv * Update lecture03.sbv * Create practicum03.sbv * Create practicum04.sbv * Create lecture05.sbv * Create practicum05.sbv * Create practicum06.sbv * Create practicum07.sbv * Create practicum08.sbv * Create practicum09.sbv * Create practicum10.sbv * Create practicum11.sbv * Create practicum12.sbv * Create practicum13.sbv * Create practicum14.sbv * Create practicum15A.sbv * Create practicum15B.sbv * Create lecture06.sbv * Create lecture07.sbv * Create lecture08.sbv * Create lecture09.sbv * Create lecture10.sbv * Create lecture11.sbv * Create lecture13.sbv * Create lecture14.sbv * twitter account + message Added an information message in case French subtitles are added to YouTube before V2 of the site is deployed. And it was time for me to create a twitter account 🙃 * Update lecture02.sbv * Update lecture02.sbv * Update practicum02.sbv * Update practicum02.sbv * Update lecture03.sbv * Update lecture05.sbv * Update practicum05.sbv * Update practicum05.sbv * Update lecture06.sbv * Update lecture06.sbv * Update practicum06.sbv * Update practicum06.sbv * Update practicum07.sbv * Update practicum07.sbv * Update practicum09.sbv * Update practicum09.sbv * Update practicum11.sbv * Update practicum12.sbv * Update practicum12.sbv * Update practicum13.sbv * Update practicum13.sbv * Update practicum13.sbv * Update practicum15A.sbv * Update practicum15A.sbv * Update docs/en/week15/practicum15A.sbv Co-authored-by: Alfredo Canziani <[email protected]>
Atcold · Oct 19, 2021 · 90fad5e · 90fad5e
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diff --git a/docs/en/week01/lecture01.sbv b/docs/en/week01/lecture01.sbv
@@ -3167,19 +3167,19 @@ It's a two layer neural net,
 where the first layer, which is symbolized by this function K here,
 
 1:21:10.830,1:21:16.550
-each unit in the first layer compares the input vector X to one of the training samples X^i's. 
+each unit in the first layer compares the input vector X to one of the training samples Xⁱ. 
 
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 OK, so you take your training samples, let's say you have a thousand of them—
 
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-so you have a thousand X^i's, from i = 1 to 1,000,
+so you have a thousand Xⁱ, from i = 1 to 1,000,
 
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 and you have some function K that is going to compare X and X^i. 
 
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-Good example of a function to compare the two is you take the dot product between X and X^i, and you pass the result
+Good example of a function to compare the two is you take the dot product between X and Xⁱ, and you pass the result
 
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 through,
@@ -3191,14 +3191,11 @@ like, exponential minus square or something. So you get a Gaussian
 response,
 
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-as a function of the distance between X and X^i, OK.
+as a function of the distance between X and Xⁱ, OK.
 
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 So it's a way of comparing to two vectors, doesn't matter what it is.
 
-1:21:46.980,1:21:48.510
-
-
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 And, then you take those scores coming out of this K function that compares the input to every sample and
 
@@ -3287,7 +3284,7 @@ that doesn't solve the problem.
 You can use a two-layer neural net, OK. So this is the top right here. The first layer is a nonlinear function
 
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-F applied to the product of a matrix W^0 by input vector; and then the second layer multiplied by the second matrix; and then
+F applied to the product of a matrix W⁰ by input vector; and then the second layer multiplied by the second matrix; and then
 
 1:24:14.040,1:24:16.040
 passes it through another non-linearity.

diff --git a/docs/en/week02/lecture02.sbv b/docs/en/week02/lecture02.sbv