correcting typos

Author: mhjensen

doc/Projects/2025/Project2/html/._Project2-bs000.html

@@ -495,7 +495,7 @@ <h3 id="part-f-classification-analysis-using-neural-networks" class="anchor">Par
 </p>
 
 <p>If you have time, you can use the functionality of <b>scikit-learn</b> and compare your neural network results with those from Logistic regression. This is optional.
-The weblink  here <a href="https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3" target="_self"><tt>https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3</tt></a>compares logistic regression and FFNN using the so-called MNIST data set. You may find several useful hints and ideas from this article. Your neural network code can implement the equivalent of logistic regression by simply setting the number of hidden layers to zero. 
+The weblink  here <a href="https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3" target="_self"><tt>https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3</tt></a>compares logistic regression and FFNN using the so-called MNIST data set. You may find several useful hints and ideas from this article. Your neural network code can implement the equivalent of logistic regression by simply setting the number of hidden layers to zero and keeping just the input and the output layers. 
 </p>
 
 <p>If you wish to compare with say Logisti Regression from <b>scikit-learn</b>, the following code uses the above data set</p>
@@ -572,7 +572,7 @@ <h3 id="required-analysis" class="anchor">Required Analysis: </h3>
 </ol>
 <h3 id="optional-note-that-you-should-include-at-least-two-of-these-in-the-report" class="anchor">Optional (Note that you should include at least two of these in the report): </h3>
 <ol>
-<li> Implement Logistic Regression as simple classification model case (equivalent to a Neural Network with one layer)</li>
+<li> Implement Logistic Regression as simple classification model case (equivalent to a Neural Network with just the output layer)</li>
 <li> Compute the gradient of the neural network with autograd, to show that it gives the same result as your hand-written backpropagation.</li>
 <li> Compare your results with results from using a machine-learning library like pytorch (https://docs.pytorch.org/tutorials/beginner/basics/buildmodel_tutorial.html)</li>
 <li> Use a more complex classification dataset instead, like the fashion MNIST (see <a href="https://www.kaggle.com/datasets/zalando-research/fashionmnist" target="_self"><tt>https://www.kaggle.com/datasets/zalando-research/fashionmnist</tt></a>)</li>

doc/Projects/2025/Project2/html/Project2-bs.html

@@ -495,7 +495,7 @@ <h3 id="part-f-classification-analysis-using-neural-networks" class="anchor">Par
 </p>
 
 <p>If you have time, you can use the functionality of <b>scikit-learn</b> and compare your neural network results with those from Logistic regression. This is optional.
-The weblink  here <a href="https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3" target="_self"><tt>https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3</tt></a>compares logistic regression and FFNN using the so-called MNIST data set. You may find several useful hints and ideas from this article. Your neural network code can implement the equivalent of logistic regression by simply setting the number of hidden layers to zero. 
+The weblink  here <a href="https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3" target="_self"><tt>https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3</tt></a>compares logistic regression and FFNN using the so-called MNIST data set. You may find several useful hints and ideas from this article. Your neural network code can implement the equivalent of logistic regression by simply setting the number of hidden layers to zero and keeping just the input and the output layers. 
 </p>
 
 <p>If you wish to compare with say Logisti Regression from <b>scikit-learn</b>, the following code uses the above data set</p>
@@ -572,7 +572,7 @@ <h3 id="required-analysis" class="anchor">Required Analysis: </h3>
 </ol>
 <h3 id="optional-note-that-you-should-include-at-least-two-of-these-in-the-report" class="anchor">Optional (Note that you should include at least two of these in the report): </h3>
 <ol>
-<li> Implement Logistic Regression as simple classification model case (equivalent to a Neural Network with one layer)</li>
+<li> Implement Logistic Regression as simple classification model case (equivalent to a Neural Network with just the output layer)</li>
 <li> Compute the gradient of the neural network with autograd, to show that it gives the same result as your hand-written backpropagation.</li>
 <li> Compare your results with results from using a machine-learning library like pytorch (https://docs.pytorch.org/tutorials/beginner/basics/buildmodel_tutorial.html)</li>
 <li> Use a more complex classification dataset instead, like the fashion MNIST (see <a href="https://www.kaggle.com/datasets/zalando-research/fashionmnist" target="_self"><tt>https://www.kaggle.com/datasets/zalando-research/fashionmnist</tt></a>)</li>

doc/Projects/2025/Project2/html/Project2.html

@@ -527,7 +527,7 @@ <h3 id="part-f-classification-analysis-using-neural-networks">Part f): Classific
 </p>
 
 <p>If you have time, you can use the functionality of <b>scikit-learn</b> and compare your neural network results with those from Logistic regression. This is optional.
-The weblink  here <a href="https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3" target="_blank"><tt>https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3</tt></a>compares logistic regression and FFNN using the so-called MNIST data set. You may find several useful hints and ideas from this article. Your neural network code can implement the equivalent of logistic regression by simply setting the number of hidden layers to zero. 
+The weblink  here <a href="https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3" target="_blank"><tt>https://medium.com/ai-in-plain-english/comparison-between-logistic-regression-and-neural-networks-in-classifying-digits-dc5e85cd93c3</tt></a>compares logistic regression and FFNN using the so-called MNIST data set. You may find several useful hints and ideas from this article. Your neural network code can implement the equivalent of logistic regression by simply setting the number of hidden layers to zero and keeping just the input and the output layers. 
 </p>
 
 <p>If you wish to compare with say Logisti Regression from <b>scikit-learn</b>, the following code uses the above data set</p>
@@ -604,7 +604,7 @@ <h3 id="required-analysis">Required Analysis: </h3>
 </ol>
 <h3 id="optional-note-that-you-should-include-at-least-two-of-these-in-the-report">Optional (Note that you should include at least two of these in the report): </h3>
 <ol>
-<li> Implement Logistic Regression as simple classification model case (equivalent to a Neural Network with one layer)</li>
+<li> Implement Logistic Regression as simple classification model case (equivalent to a Neural Network with just the output layer)</li>
 <li> Compute the gradient of the neural network with autograd, to show that it gives the same result as your hand-written backpropagation.</li>
 <li> Compare your results with results from using a machine-learning library like pytorch (https://docs.pytorch.org/tutorials/beginner/basics/buildmodel_tutorial.html)</li>
 <li> Use a more complex classification dataset instead, like the fashion MNIST (see <a href="https://www.kaggle.com/datasets/zalando-research/fashionmnist" target="_blank"><tt>https://www.kaggle.com/datasets/zalando-research/fashionmnist</tt></a>)</li>