updating week 5

Author: mhjensen

doc/pub/week5/html/week5-bs.html

@@ -46,10 +46,11 @@
                None,
                'a-deep-cnn-model-from-raschka-et-al-https-github-com-rasbt-machine-learning-book'),
               ('Key Idea', 2, None, 'key-idea'),
-              ('How to do image compression before the era of deep learning',
+              ('How did we do image compression before the era of deep '
+               'learning',
                2,
                None,
-               'how-to-do-image-compression-before-the-era-of-deep-learning'),
+               'how-did-we-do-image-compression-before-the-era-of-deep-learning'),
               ('The SVD example', 2, None, 'the-svd-example'),
               ('Examples of CNN setups', 2, None, 'examples-of-cnn-setups'),
               ('Summarizing: Performing a general discrete convolution ("From '
@@ -248,7 +249,7 @@
      <!-- navigation toc: --> <li><a href="#cnns-in-brief" style="font-size: 80%;"><b>CNNs in brief</b></a></li>
      <!-- navigation toc: --> <li><a href="#a-deep-cnn-model-from-raschka-et-al-https-github-com-rasbt-machine-learning-book" style="font-size: 80%;"><b>A deep CNN model ("From Raschka et al":"https://github.com/rasbt/machine-learning-book")</b></a></li>
      <!-- navigation toc: --> <li><a href="#key-idea" style="font-size: 80%;"><b>Key Idea</b></a></li>
-     <!-- navigation toc: --> <li><a href="#how-to-do-image-compression-before-the-era-of-deep-learning" style="font-size: 80%;"><b>How to do image compression before the era of deep learning</b></a></li>
+     <!-- navigation toc: --> <li><a href="#how-did-we-do-image-compression-before-the-era-of-deep-learning" style="font-size: 80%;"><b>How did we do image compression before the era of deep learning</b></a></li>
      <!-- navigation toc: --> <li><a href="#the-svd-example" style="font-size: 80%;"><b>The SVD example</b></a></li>
      <!-- navigation toc: --> <li><a href="#examples-of-cnn-setups" style="font-size: 80%;"><b>Examples of CNN setups</b></a></li>
      <!-- navigation toc: --> <li><a href="#summarizing-performing-a-general-discrete-convolution-from-raschka-et-al-https-github-com-rasbt-machine-learning-book" style="font-size: 80%;"><b>Summarizing: Performing a general discrete convolution ("From Raschka et al":"https://github.com/rasbt/machine-learning-book")</b></a></li>
@@ -423,7 +424,7 @@ <h2 id="key-idea" class="anchor">Key Idea </h2>
 <p>We say we perform a filtering (convolution is the mathematical operation). </p>
 
 <!-- !split -->
-<h2 id="how-to-do-image-compression-before-the-era-of-deep-learning" class="anchor">How to do image compression before the era of deep learning </h2>
+<h2 id="how-did-we-do-image-compression-before-the-era-of-deep-learning" class="anchor">How did we do image compression before the era of deep learning </h2>
 
 <p>The singular-value decomposition (SVD) algorithm has been for decades one of the standard ways of compressing images.
 The <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter2.html#the-singular-value-decomposition" target="_self">lectures on the SVD</a> give many of the essential details concerning the SVD.

doc/pub/week5/html/week5-reveal.html

@@ -268,7 +268,7 @@ <h2 id="key-idea">Key Idea </h2>
 </section>
 
 <section>
-<h2 id="how-to-do-image-compression-before-the-era-of-deep-learning">How to do image compression before the era of deep learning </h2>
+<h2 id="how-did-we-do-image-compression-before-the-era-of-deep-learning">How did we do image compression before the era of deep learning </h2>
 
 <p>The singular-value decomposition (SVD) algorithm has been for decades one of the standard ways of compressing images.
 The <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter2.html#the-singular-value-decomposition" target="_blank">lectures on the SVD</a> give many of the essential details concerning the SVD.

doc/pub/week5/html/week5-solarized.html

@@ -73,10 +73,11 @@
                None,
                'a-deep-cnn-model-from-raschka-et-al-https-github-com-rasbt-machine-learning-book'),
               ('Key Idea', 2, None, 'key-idea'),
-              ('How to do image compression before the era of deep learning',
+              ('How did we do image compression before the era of deep '
+               'learning',
                2,
                None,
-               'how-to-do-image-compression-before-the-era-of-deep-learning'),
+               'how-did-we-do-image-compression-before-the-era-of-deep-learning'),
               ('The SVD example', 2, None, 'the-svd-example'),
               ('Examples of CNN setups', 2, None, 'examples-of-cnn-setups'),
               ('Summarizing: Performing a general discrete convolution ("From '
@@ -344,7 +345,7 @@ <h2 id="key-idea">Key Idea </h2>
 <p>We say we perform a filtering (convolution is the mathematical operation). </p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-<h2 id="how-to-do-image-compression-before-the-era-of-deep-learning">How to do image compression before the era of deep learning </h2>
+<h2 id="how-did-we-do-image-compression-before-the-era-of-deep-learning">How did we do image compression before the era of deep learning </h2>
 
 <p>The singular-value decomposition (SVD) algorithm has been for decades one of the standard ways of compressing images.
 The <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter2.html#the-singular-value-decomposition" target="_blank">lectures on the SVD</a> give many of the essential details concerning the SVD.

doc/pub/week5/html/week5.html

@@ -150,10 +150,11 @@
                None,
                'a-deep-cnn-model-from-raschka-et-al-https-github-com-rasbt-machine-learning-book'),
               ('Key Idea', 2, None, 'key-idea'),
-              ('How to do image compression before the era of deep learning',
+              ('How did we do image compression before the era of deep '
+               'learning',
                2,
                None,
-               'how-to-do-image-compression-before-the-era-of-deep-learning'),
+               'how-did-we-do-image-compression-before-the-era-of-deep-learning'),
               ('The SVD example', 2, None, 'the-svd-example'),
               ('Examples of CNN setups', 2, None, 'examples-of-cnn-setups'),
               ('Summarizing: Performing a general discrete convolution ("From '
@@ -421,7 +422,7 @@ <h2 id="key-idea">Key Idea </h2>
 <p>We say we perform a filtering (convolution is the mathematical operation). </p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-<h2 id="how-to-do-image-compression-before-the-era-of-deep-learning">How to do image compression before the era of deep learning </h2>
+<h2 id="how-did-we-do-image-compression-before-the-era-of-deep-learning">How did we do image compression before the era of deep learning </h2>
 
 <p>The singular-value decomposition (SVD) algorithm has been for decades one of the standard ways of compressing images.
 The <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/chapter2.html#the-singular-value-decomposition" target="_blank">lectures on the SVD</a> give many of the essential details concerning the SVD.