small typo

doc/pub/week38/html/._week38-bs012.html

@@ -247,7 +247,10 @@ in the data set (see the full example below).
 <p>One simple way to get a discrete output is to have sign
 functions that map the output of a linear regressor to values \( \{0,1\} \),
 \( f(s_i)=sign(s_i)=1 \) if \( s_i\ge 0 \) and 0 if otherwise. 
-We will encounter this model in our first demonstration of neural networks. Historically it is called the ``perceptron" model in the machine learning
+We will encounter this model in our first demonstration of neural networks.
+</p>
+
+<p>Historically it is called the <b>perceptron</b> model in the machine learning
 literature. This model is extremely simple. However, in many cases it is more
 favorable to use a ``soft" classifier that outputs
 the probability of a given category. This leads us to the logistic function.

doc/pub/week38/html/week38-reveal.html

@@ -591,7 +591,10 @@ in the data set (see the full example below).
 <p>One simple way to get a discrete output is to have sign
 functions that map the output of a linear regressor to values \( \{0,1\} \),
 \( f(s_i)=sign(s_i)=1 \) if \( s_i\ge 0 \) and 0 if otherwise. 
-We will encounter this model in our first demonstration of neural networks. Historically it is called the ``perceptron" model in the machine learning
+We will encounter this model in our first demonstration of neural networks.
+</p>
+
+<p>Historically it is called the <b>perceptron</b> model in the machine learning
 literature. This model is extremely simple. However, in many cases it is more
 favorable to use a ``soft" classifier that outputs
 the probability of a given category. This leads us to the logistic function.

doc/pub/week38/html/week38-solarized.html

@@ -552,7 +552,10 @@ in the data set (see the full example below).
 <p>One simple way to get a discrete output is to have sign
 functions that map the output of a linear regressor to values \( \{0,1\} \),
 \( f(s_i)=sign(s_i)=1 \) if \( s_i\ge 0 \) and 0 if otherwise. 
-We will encounter this model in our first demonstration of neural networks. Historically it is called the ``perceptron" model in the machine learning
+We will encounter this model in our first demonstration of neural networks.
+</p>
+
+<p>Historically it is called the <b>perceptron</b> model in the machine learning
 literature. This model is extremely simple. However, in many cases it is more
 favorable to use a ``soft" classifier that outputs
 the probability of a given category. This leads us to the logistic function.

doc/pub/week38/html/week38.html

@@ -629,7 +629,10 @@ in the data set (see the full example below).
 <p>One simple way to get a discrete output is to have sign
 functions that map the output of a linear regressor to values \( \{0,1\} \),
 \( f(s_i)=sign(s_i)=1 \) if \( s_i\ge 0 \) and 0 if otherwise. 
-We will encounter this model in our first demonstration of neural networks. Historically it is called the ``perceptron" model in the machine learning
+We will encounter this model in our first demonstration of neural networks.
+</p>
+
+<p>Historically it is called the <b>perceptron</b> model in the machine learning
 literature. This model is extremely simple. However, in many cases it is more
 favorable to use a ``soft" classifier that outputs
 the probability of a given category. This leads us to the logistic function.

doc/src/week38/week38.do.txt

@@ -354,7 +354,9 @@ in the data set (see the full example below).
 One simple way to get a discrete output is to have sign
 functions that map the output of a linear regressor to values $\{0,1\}$,
 $f(s_i)=sign(s_i)=1$ if $s_i\ge 0$ and 0 if otherwise. 
-We will encounter this model in our first demonstration of neural networks. Historically it is called the ``perceptron" model in the machine learning
+We will encounter this model in our first demonstration of neural networks.
+
+Historically it is called the _perceptron_ model in the machine learning
 literature. This model is extremely simple. However, in many cases it is more
 favorable to use a ``soft" classifier that outputs
 the probability of a given category. This leads us to the logistic function.