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diff --git a/_images/bfc6fbc48f41e2f18feb1029f323d0784676280b1074d30f6ccd70a39adddea4.png b/_images/bfc6fbc48f41e2f18feb1029f323d0784676280b1074d30f6ccd70a39adddea4.png
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diff --git a/_sources/lectures/101-Giulia-lectures/19_intro_to_computer-vision.ipynb b/_sources/lectures/101-Giulia-lectures/19_intro_to_computer-vision.ipynb
diff --git a/lectures/101-Giulia-lectures/07_linear-models.html b/lectures/101-Giulia-lectures/07_linear-models.html
@@ -1743,8 +1743,8 @@ <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-86d21999-b1ea-4145-a101-937a2842e45c">
-<span class="eqno">(2)<a class="headerlink" href="#equation-86d21999-b1ea-4145-a101-937a2842e45c" title="Permalink to this equation">#</a></span>\[\begin{equation}
+<div class="amsmath math notranslate nohighlight" id="equation-9f0672fe-afc6-4246-96f7-4a477d174e74">
+<span class="eqno">(2)<a class="headerlink" href="#equation-9f0672fe-afc6-4246-96f7-4a477d174e74" 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/101-Giulia-lectures/19_intro_to_computer-vision.html b/lectures/101-Giulia-lectures/19_intro_to_computer-vision.html
diff --git a/lectures/notes/07_linear-models.html b/lectures/notes/07_linear-models.html
@@ -1687,8 +1687,8 @@ <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-85c97a9c-b6a8-4121-8a16-351c9d2306b6">
-<span class="eqno">(1)<a class="headerlink" href="#equation-85c97a9c-b6a8-4121-8a16-351c9d2306b6" title="Permalink to this equation">#</a></span>\[\begin{equation}
+<div class="amsmath math notranslate nohighlight" id="equation-a104d6db-c6a8-4f98-9b0f-bbdea9b96eb3">
+<span class="eqno">(1)<a class="headerlink" href="#equation-a104d6db-c6a8-4f98-9b0f-bbdea9b96eb3" 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/objects.inv b/objects.inv
diff --git a/searchindex.js b/searchindex.js