small typos

doc/pub/week35/html/._week35-bs057.html

@@ -377,7 +377,7 @@ function, that is we have
 </p>
 
 $$
-\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}^T\partial \boldsymbol{\beta}} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.  
+\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.  
 $$
 
 <p>This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).</p>

doc/pub/week35/html/._week35-bs058.html

@@ -405,7 +405,7 @@ with a factor \( 1/(n-1) \). This is called <a href="https://mathworld.wolfram.c
 method corrects the bias in the estimation of the population variance
 and covariance. It also partially corrects the bias in the estimation
 of the population standard deviation. If you use a library like
-<b>Scikit-Learn</b> or <b>nunmpy's</b> function calculate the covariance, this
+<b>Scikit-Learn</b> or <b>nunmpy's</b> function to calculate the covariance, this
 quantity will be computed with a factor \( 1/(n-1) \).
 </p>
 

doc/pub/week35/html/week35-reveal.html

@@ -2950,7 +2950,7 @@ function, that is we have
 
 <p>&nbsp;<br>
 $$
-\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}^T\partial \boldsymbol{\beta}} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.  
+\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.  
 $$
 <p>&nbsp;<br>
 
@@ -3018,7 +3018,7 @@ with a factor \( 1/(n-1) \). This is called <a href="https://mathworld.wolfram.c
 method corrects the bias in the estimation of the population variance
 and covariance. It also partially corrects the bias in the estimation
 of the population standard deviation. If you use a library like
-<b>Scikit-Learn</b> or <b>nunmpy's</b> function calculate the covariance, this
+<b>Scikit-Learn</b> or <b>nunmpy's</b> function to calculate the covariance, this
 quantity will be computed with a factor \( 1/(n-1) \).
 </p>
 </section>

doc/pub/week35/html/week35-solarized.html

@@ -2823,7 +2823,7 @@ function, that is we have
 </p>
 
 $$
-\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}^T\partial \boldsymbol{\beta}} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.  
+\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.  
 $$
 
 <p>This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).</p>
@@ -2879,7 +2879,7 @@ with a factor \( 1/(n-1) \). This is called <a href="https://mathworld.wolfram.c
 method corrects the bias in the estimation of the population variance
 and covariance. It also partially corrects the bias in the estimation
 of the population standard deviation. If you use a library like
-<b>Scikit-Learn</b> or <b>nunmpy's</b> function calculate the covariance, this
+<b>Scikit-Learn</b> or <b>nunmpy's</b> function to calculate the covariance, this
 quantity will be computed with a factor \( 1/(n-1) \).
 </p>
 

doc/pub/week35/html/week35.html

@@ -2900,7 +2900,7 @@ function, that is we have
 </p>
 
 $$
-\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}^T\partial \boldsymbol{\beta}} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.  
+\frac{\partial^2 C(\boldsymbol{\beta})}{\partial \boldsymbol{\beta}\partial \boldsymbol{\beta}^T} =\frac{2}{n}\boldsymbol{X}^T\boldsymbol{X}.  
 $$
 
 <p>This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).</p>
@@ -2956,7 +2956,7 @@ with a factor \( 1/(n-1) \). This is called <a href="https://mathworld.wolfram.c
 method corrects the bias in the estimation of the population variance
 and covariance. It also partially corrects the bias in the estimation
 of the population standard deviation. If you use a library like
-<b>Scikit-Learn</b> or <b>nunmpy's</b> function calculate the covariance, this
+<b>Scikit-Learn</b> or <b>nunmpy's</b> function to calculate the covariance, this
 quantity will be computed with a factor \( 1/(n-1) \).
 </p>
 

doc/src/week35/week35.do.txt

@@ -2132,7 +2132,7 @@ function, that is we have
 
 !bt
 \[
-\frac{\partial^2 C(\bm{\beta})}{\partial \bm{\beta}^T\partial \bm{\beta}} =\frac{2}{n}\bm{X}^T\bm{X}.  
+\frac{\partial^2 C(\bm{\beta})}{\partial \bm{\beta}\partial \bm{\beta}^T} =\frac{2}{n}\bm{X}^T\bm{X}.  
 \]
 !et
 This quantity defines was what is called the Hessian matrix (the second derivative of a function we want to optimize).
@@ -2194,7 +2194,7 @@ with a factor $1/(n-1)$. This is called "Bessel's correction":"https://mathworld
 method corrects the bias in the estimation of the population variance
 and covariance. It also partially corrects the bias in the estimation
 of the population standard deviation. If you use a library like
-_Scikit-Learn_ or _nunmpy's_ function calculate the covariance, this
+_Scikit-Learn_ or _nunmpy's_ function to calculate the covariance, this
 quantity will be computed with a factor $1/(n-1)$.
 
 !split