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diff --git a/_sources/lectures/102-Varada-lectures/class_demos/demo_16-dbscan-hierarchical.ipynb b/_sources/lectures/102-Varada-lectures/class_demos/demo_16-dbscan-hierarchical.ipynb
diff --git a/lectures/101-Giulia-lectures/07_linear-models.html b/lectures/101-Giulia-lectures/07_linear-models.html
@@ -1747,8 +1747,8 @@ <h4>Predicting with learned weights<a class="headerlink" href="#predicting-with-
 <p>In our case, for values for the coefficient of <em>boring</em> &lt; -3.36, the prediction would be negative.</p>
 <p>A linear model learns these coefficients or weights from the training data!</p>
 <p>So a linear classifier is a linear function of the input <code class="docutils literal notranslate"><span class="pre">X</span></code>, followed by a threshold.</p>
-<div class="amsmath math notranslate nohighlight" id="equation-3e3f63a8-594f-4ae7-bd3f-4bdec7436908">
-<span class="eqno">(2)<a class="headerlink" href="#equation-3e3f63a8-594f-4ae7-bd3f-4bdec7436908" title="Permalink to this equation">#</a></span>\[\begin{equation}
+<div class="amsmath math notranslate nohighlight" id="equation-c877b458-39d5-4b7f-8449-bf764f414d24">
+<span class="eqno">(2)<a class="headerlink" href="#equation-c877b458-39d5-4b7f-8449-bf764f414d24" title="Permalink to this equation">#</a></span>\[\begin{equation}
 \begin{split}
 z =&amp; w_1x_1 + \dots + w_dx_d + b\\
 =&amp; w^Tx + b

diff --git a/lectures/102-Varada-lectures/class_demos/demo_16-dbscan-hierarchical.html b/lectures/102-Varada-lectures/class_demos/demo_16-dbscan-hierarchical.html
diff --git a/lectures/notes/07_linear-models.html b/lectures/notes/07_linear-models.html
@@ -1691,8 +1691,8 @@ <h4>Predicting with learned weights<a class="headerlink" href="#predicting-with-
 <p>In our case, for values for the coefficient of <em>boring</em> &lt; -3.36, the prediction would be negative.</p>
 <p>A linear model learns these coefficients or weights from the training data!</p>
 <p>So a linear classifier is a linear function of the input <code class="docutils literal notranslate"><span class="pre">X</span></code>, followed by a threshold.</p>
-<div class="amsmath math notranslate nohighlight" id="equation-0a05c5c0-9cab-476a-8307-ba0bff9f1178">
-<span class="eqno">(1)<a class="headerlink" href="#equation-0a05c5c0-9cab-476a-8307-ba0bff9f1178" title="Permalink to this equation">#</a></span>\[\begin{equation}
+<div class="amsmath math notranslate nohighlight" id="equation-8f66f637-a731-4190-8fc1-ced4489ce9b4">
+<span class="eqno">(1)<a class="headerlink" href="#equation-8f66f637-a731-4190-8fc1-ced4489ce9b4" title="Permalink to this equation">#</a></span>\[\begin{equation}
 \begin{split}
 z =&amp; w_1x_1 + \dots + w_dx_d + b\\
 =&amp; w^Tx + b

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