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@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 4439ad46c51a0527f916642d49c5f71a
+config: 790b1f6d55ecd7a900234d71881ced93
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/_sources/eigenvalues/diagonalization.md b/_sources/eigenvalues/diagonalization.md
@@ -103,7 +103,7 @@ $$
 c_A(x) = \pm \prod_{i=1}^k (x - \lambda_i)^{m_i}
 $$
 
-where $\lambda_1, \dots, \lambda_k$ are the *distinct* eigenvalues of $A$. The **algebraic multiplicity** of $\lambda_i$ is the power $m_i$ in the factored charactersitic polynomial. In other words, the algebraic multiplicity of $\lambda_i$ is the number of times $\lambda_i$ occurs as a root of the characteristic polynomial $c_A(x)$.
+where $\lambda_1, \dots, \lambda_k$ are the *distinct* eigenvalues of $A$. The **algebraic multiplicity** of $\lambda_i$ is the power $m_i$ in the factored characteristic polynomial. In other words, the algebraic multiplicity of $\lambda_i$ is the number of times $\lambda_i$ occurs as a root of the characteristic polynomial $c_A(x)$.
 ```
 
 ```{div} definition

diff --git a/_sources/eigenvalues/svd.md b/_sources/eigenvalues/svd.md
@@ -646,7 +646,7 @@ P = \left[ \begin{array}{rrr} 0 & \phantom{+}1 & 0 \\ 1 & 0 & 0 \\ 0 & 0 & -1 \e
 $$
 
 $$
-Q = \left[ \begin{array}{rrr} 1 & \phantom{+}1 & 1 \\ -2 & 1 & 0 \\ 1 & 1 & -1 \end{array} \right]
+Q = \left[ \begin{array}{rrr} -1/\sqrt{6} & \phantom{+}1/\sqrt{3} & -1/\sqrt{2} \\ 2/\sqrt{6} & 1/\sqrt{3} & 0\phantom{--} \\ -1/\sqrt{6} & 1/\sqrt{3} & 1/\sqrt{2} \end{array} \right]
 $$
 ```
 ````
@@ -683,7 +683,13 @@ Find the weight vectors for the data matrix $X$ representing the points:
 ```
 
 ```{dropdown} Solution
+$$
+\boldsymbol{w}_1 = \frac{1}{2 \sqrt{20 - 2 \sqrt{10}}} \begin{bmatrix} 6 \\ 2(\sqrt{10} - 1) \end{bmatrix}
+$$
 
+$$
+\boldsymbol{w}_2 = \frac{1}{2 \sqrt{20 - 2 \sqrt{10}}} \begin{bmatrix} 2(\sqrt{10} - 1) \\ -6 \end{bmatrix}
+$$
 ```
 ````
 
@@ -695,10 +701,6 @@ X^T X = \begin{bmatrix} 2 & 0 & 0 & 0 \\ 0 & 1.5 & 0 & 0 \\ 0 & 0 & 2 & 1 \\ 0 &
 $$
 
 Find all the weight vectors of $X$.
-
-```{dropdown} Solution
-
-```
 ````
 
 ````{div} exercise
@@ -709,10 +711,6 @@ A(\boldsymbol{x} + \Delta \boldsymbol{x}) = \boldsymbol{b} + \Delta \boldsymbol{
 $$
 
 Describe the unit vector $\Delta \boldsymbol{b}$ that will produce the largest change $\| \Delta \boldsymbol{x} \|$.
-
-```{dropdown} Solution
-
-```
 ````
 
 ````{div} exercise
@@ -729,10 +727,6 @@ A = \left[ \begin{array}{rrr} 1 & 1 & 1 \\ 1 & 0 & -2 \\ 1 & -1 & 1 \end{array}
 $$
 
 (Note: the columns of $A$ are orthogonal.)
-
-```{dropdown} Solution
-
-```
 ````
 
 ````{div} exercise
@@ -743,8 +737,4 @@ A_k = \sum_{i=1}^k \sigma_i \boldsymbol{p}_i \boldsymbol{q}_i^T
 $$
 
 Describe the singular value decomposition of $A - A_k$.
-
-```{dropdown} Solution
-
-```
 ````
diff --git a/_sources/orthogonality/least_squares.md b/_sources/orthogonality/least_squares.md
@@ -160,7 +160,7 @@ f_1(t_m) & f_2(t_m) & \cdots & f_n(t_m)
 \boldsymbol{c} = \begin{bmatrix} c_1 \\ c_2 \\ \vdots \\ c_n \end{bmatrix}
 $$
 
-We assume that $m \geq n$ and $f_1,\dots,f_n$ are linearly independenty (which implies $\mathrm{rank}(A) = n$). Therefore the vector of coefficients $\boldsymbol{c}$ is the least squares approximation of the system $A \boldsymbol{c} \approx \boldsymbol{y}$.
+We assume that $m \geq n$ and $f_1,\dots,f_n$ are linearly independently (which implies $\mathrm{rank}(A) = n$). Therefore the vector of coefficients $\boldsymbol{c}$ is the least squares approximation of the system $A \boldsymbol{c} \approx \boldsymbol{y}$.
 ```
 
 ## Exercises

diff --git a/eigenvalues/diagonalization.html b/eigenvalues/diagonalization.html
@@ -479,7 +479,7 @@ <h2>Diagonalization<a class="headerlink" href="#id1" title="Permalink to this he
 \[
 c_A(x) = \pm \prod_{i=1}^k (x - \lambda_i)^{m_i}
 \]</div>
-<p>where <span class="math notranslate nohighlight">\(\lambda_1, \dots, \lambda_k\)</span> are the <em>distinct</em> eigenvalues of <span class="math notranslate nohighlight">\(A\)</span>. The <strong>algebraic multiplicity</strong> of <span class="math notranslate nohighlight">\(\lambda_i\)</span> is the power <span class="math notranslate nohighlight">\(m_i\)</span> in the factored charactersitic polynomial. In other words, the algebraic multiplicity of <span class="math notranslate nohighlight">\(\lambda_i\)</span> is the number of times <span class="math notranslate nohighlight">\(\lambda_i\)</span> occurs as a root of the characteristic polynomial <span class="math notranslate nohighlight">\(c_A(x)\)</span>.</p>
+<p>where <span class="math notranslate nohighlight">\(\lambda_1, \dots, \lambda_k\)</span> are the <em>distinct</em> eigenvalues of <span class="math notranslate nohighlight">\(A\)</span>. The <strong>algebraic multiplicity</strong> of <span class="math notranslate nohighlight">\(\lambda_i\)</span> is the power <span class="math notranslate nohighlight">\(m_i\)</span> in the factored characteristic polynomial. In other words, the algebraic multiplicity of <span class="math notranslate nohighlight">\(\lambda_i\)</span> is the number of times <span class="math notranslate nohighlight">\(\lambda_i\)</span> occurs as a root of the characteristic polynomial <span class="math notranslate nohighlight">\(c_A(x)\)</span>.</p>
 </div>
 <div class="definition docutils">
 <p>Let <span class="math notranslate nohighlight">\(\lambda\)</span> be an eigenvalue of <span class="math notranslate nohighlight">\(A\)</span>. The <strong>geometric multiplicity</strong> of <span class="math notranslate nohighlight">\(\lambda\)</span> is the number of linearly independent eigenvectors corresponding to <span class="math notranslate nohighlight">\(\lambda\)</span>. In other words, the geometric multiplicity is the dimension of the <strong>eigenspace</strong> for <span class="math notranslate nohighlight">\(\lambda\)</span></p>

diff --git a/eigenvalues/svd.html b/eigenvalues/svd.html
@@ -1002,7 +1002,7 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this he
 \end{split}\]</div>
 <div class="math notranslate nohighlight">
 \[\begin{split}
-Q = \left[ \begin{array}{rrr} 1 &amp; \phantom{+}1 &amp; 1 \\ -2 &amp; 1 &amp; 0 \\ 1 &amp; 1 &amp; -1 \end{array} \right]
+Q = \left[ \begin{array}{rrr} -1/\sqrt{6} &amp; \phantom{+}1/\sqrt{3} &amp; -1/\sqrt{2} \\ 2/\sqrt{6} &amp; 1/\sqrt{3} &amp; 0\phantom{--} \\ -1/\sqrt{6} &amp; 1/\sqrt{3} &amp; 1/\sqrt{2} \end{array} \right]
 \end{split}\]</div>
 </div>
 </details></div>
@@ -1054,6 +1054,14 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this he
 <div class="sd-summary-up docutils">
 <svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-up" viewBox="0 0 24 24" aria-hidden="true"><path fill-rule="evenodd" d="M18.78 15.28a.75.75 0 000-1.06l-6.25-6.25a.75.75 0 00-1.06 0l-6.25 6.25a.75.75 0 101.06 1.06L12 9.56l5.72 5.72a.75.75 0 001.06 0z"></path></svg></div>
 </summary><div class="sd-summary-content sd-card-body docutils">
+<div class="math notranslate nohighlight">
+\[\begin{split}
+\boldsymbol{w}_1 = \frac{1}{2 \sqrt{20 - 2 \sqrt{10}}} \begin{bmatrix} 6 \\ 2(\sqrt{10} - 1) \end{bmatrix}
+\end{split}\]</div>
+<div class="math notranslate nohighlight">
+\[\begin{split}
+\boldsymbol{w}_2 = \frac{1}{2 \sqrt{20 - 2 \sqrt{10}}} \begin{bmatrix} 2(\sqrt{10} - 1) \\ -6 \end{bmatrix}
+\end{split}\]</div>
 </div>
 </details></div>
 <div class="exercise docutils">
@@ -1063,31 +1071,15 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this he
 X^T X = \begin{bmatrix} 2 &amp; 0 &amp; 0 &amp; 0 \\ 0 &amp; 1.5 &amp; 0 &amp; 0 \\ 0 &amp; 0 &amp; 2 &amp; 1 \\ 0 &amp; 0 &amp; 1 &amp; 2 \end{bmatrix}
 \end{split}\]</div>
 <p>Find all the weight vectors of <span class="math notranslate nohighlight">\(X\)</span>.</p>
-<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3">
-<summary class="sd-summary-title sd-card-header">
-Solution<div class="sd-summary-down docutils">
-<svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-down" viewBox="0 0 24 24" aria-hidden="true"><path fill-rule="evenodd" d="M5.22 8.72a.75.75 0 000 1.06l6.25 6.25a.75.75 0 001.06 0l6.25-6.25a.75.75 0 00-1.06-1.06L12 14.44 6.28 8.72a.75.75 0 00-1.06 0z"></path></svg></div>
-<div class="sd-summary-up docutils">
-<svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-up" viewBox="0 0 24 24" aria-hidden="true"><path fill-rule="evenodd" d="M18.78 15.28a.75.75 0 000-1.06l-6.25-6.25a.75.75 0 00-1.06 0l-6.25 6.25a.75.75 0 101.06 1.06L12 9.56l5.72 5.72a.75.75 0 001.06 0z"></path></svg></div>
-</summary><div class="sd-summary-content sd-card-body docutils">
 </div>
-</details></div>
 <div class="exercise docutils">
 <p>Suppose we want to solve a system <span class="math notranslate nohighlight">\(A \boldsymbol{x} = \boldsymbol{b}\)</span>. A small change <span class="math notranslate nohighlight">\(\Delta \boldsymbol{b}\)</span> produces a change in the solution</p>
 <div class="math notranslate nohighlight">
 \[
 A(\boldsymbol{x} + \Delta \boldsymbol{x}) = \boldsymbol{b} + \Delta \boldsymbol{b}
 \]</div>
 <p>Describe the unit vector <span class="math notranslate nohighlight">\(\Delta \boldsymbol{b}\)</span> that will produce the largest change <span class="math notranslate nohighlight">\(\| \Delta \boldsymbol{x} \|\)</span>.</p>
-<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3">
-<summary class="sd-summary-title sd-card-header">
-Solution<div class="sd-summary-down docutils">
-<svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-down" viewBox="0 0 24 24" aria-hidden="true"><path fill-rule="evenodd" d="M5.22 8.72a.75.75 0 000 1.06l6.25 6.25a.75.75 0 001.06 0l6.25-6.25a.75.75 0 00-1.06-1.06L12 14.44 6.28 8.72a.75.75 0 00-1.06 0z"></path></svg></div>
-<div class="sd-summary-up docutils">
-<svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-up" viewBox="0 0 24 24" aria-hidden="true"><path fill-rule="evenodd" d="M18.78 15.28a.75.75 0 000-1.06l-6.25-6.25a.75.75 0 00-1.06 0l-6.25 6.25a.75.75 0 101.06 1.06L12 9.56l5.72 5.72a.75.75 0 001.06 0z"></path></svg></div>
-</summary><div class="sd-summary-content sd-card-body docutils">
 </div>
-</details></div>
 <div class="exercise docutils">
 <p>Find the rank 2 pseudo inverse</p>
 <div class="math notranslate nohighlight">
@@ -1100,31 +1092,15 @@ <h2>Exercises<a class="headerlink" href="#exercises" title="Permalink to this he
 A = \left[ \begin{array}{rrr} 1 &amp; 1 &amp; 1 \\ 1 &amp; 0 &amp; -2 \\ 1 &amp; -1 &amp; 1 \end{array} \right]
 \end{split}\]</div>
 <p>(Note: the columns of <span class="math notranslate nohighlight">\(A\)</span> are orthogonal.)</p>
-<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3">
-<summary class="sd-summary-title sd-card-header">
-Solution<div class="sd-summary-down docutils">
-<svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-down" viewBox="0 0 24 24" aria-hidden="true"><path fill-rule="evenodd" d="M5.22 8.72a.75.75 0 000 1.06l6.25 6.25a.75.75 0 001.06 0l6.25-6.25a.75.75 0 00-1.06-1.06L12 14.44 6.28 8.72a.75.75 0 00-1.06 0z"></path></svg></div>
-<div class="sd-summary-up docutils">
-<svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-up" viewBox="0 0 24 24" aria-hidden="true"><path fill-rule="evenodd" d="M18.78 15.28a.75.75 0 000-1.06l-6.25-6.25a.75.75 0 00-1.06 0l-6.25 6.25a.75.75 0 101.06 1.06L12 9.56l5.72 5.72a.75.75 0 001.06 0z"></path></svg></div>
-</summary><div class="sd-summary-content sd-card-body docutils">
 </div>
-</details></div>
 <div class="exercise docutils">
 <p>Let <span class="math notranslate nohighlight">\(A\)</span> be a <span class="math notranslate nohighlight">\(m \times n\)</span> matrix with singular value decomposition <span class="math notranslate nohighlight">\(A = P \Sigma Q^T\)</span>. Let <span class="math notranslate nohighlight">\(k &lt; \min\{m,n\}\)</span> and let</p>
 <div class="math notranslate nohighlight">
 \[
 A_k = \sum_{i=1}^k \sigma_i \boldsymbol{p}_i \boldsymbol{q}_i^T
 \]</div>
 <p>Describe the singular value decomposition of <span class="math notranslate nohighlight">\(A - A_k\)</span>.</p>
-<details class="sd-sphinx-override sd-dropdown sd-card sd-mb-3">
-<summary class="sd-summary-title sd-card-header">
-Solution<div class="sd-summary-down docutils">
-<svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-down" viewBox="0 0 24 24" aria-hidden="true"><path fill-rule="evenodd" d="M5.22 8.72a.75.75 0 000 1.06l6.25 6.25a.75.75 0 001.06 0l6.25-6.25a.75.75 0 00-1.06-1.06L12 14.44 6.28 8.72a.75.75 0 00-1.06 0z"></path></svg></div>
-<div class="sd-summary-up docutils">
-<svg version="1.1" width="1.5em" height="1.5em" class="sd-octicon sd-octicon-chevron-up" viewBox="0 0 24 24" aria-hidden="true"><path fill-rule="evenodd" d="M18.78 15.28a.75.75 0 000-1.06l-6.25-6.25a.75.75 0 00-1.06 0l-6.25 6.25a.75.75 0 101.06 1.06L12 9.56l5.72 5.72a.75.75 0 001.06 0z"></path></svg></div>
-</summary><div class="sd-summary-content sd-card-body docutils">
 </div>
-</details></div>
 </section>
 </section>
 

diff --git a/notebooks/01_linear_systems.html b/notebooks/01_linear_systems.html
@@ -647,6 +647,9 @@ <h2>Example: Resistor Network<a class="headerlink" href="#example-resistor-netwo
  [ 5.]]
 </pre></div>
 </div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
 </div>
 </div>
 </section>

diff --git a/notebooks/03_polynomial_interpolation.html b/notebooks/03_polynomial_interpolation.html
@@ -612,7 +612,7 @@ <h2>Example 4<a class="headerlink" href="#example-4" title="Permalink to this he
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/54bb25a1f17bf5554019184852d34ee28ad5f99c1117d0ea764da6ce104fa526.png" src="../_images/54bb25a1f17bf5554019184852d34ee28ad5f99c1117d0ea764da6ce104fa526.png" />
+<img alt="../_images/d2617a8cd6660d08b687e3d6e916943125722408d2d59dc7c17754ad0213d61a.png" src="../_images/d2617a8cd6660d08b687e3d6e916943125722408d2d59dc7c17754ad0213d61a.png" />
 </div>
 </div>
 <p>Yikes! The interpolating polynomial is very sensitive to small changes in the <span class="math notranslate nohighlight">\(y\)</span> values! That’s because the condition number is large!</p>

diff --git a/notebooks/04_spline_interpolation.html b/notebooks/04_spline_interpolation.html
@@ -573,7 +573,7 @@ <h2>Example 2<a class="headerlink" href="#example-2" title="Permalink to this he
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/ebaa181f0a491915b4a182951689878b55fd9d8a157318580da19340cef54bb3.png" src="../_images/ebaa181f0a491915b4a182951689878b55fd9d8a157318580da19340cef54bb3.png" />
+<img alt="../_images/21eb4f509f3b727e26d3294cd8fff95754db5b33c22e386509b51c01827dc09b.png" src="../_images/21eb4f509f3b727e26d3294cd8fff95754db5b33c22e386509b51c01827dc09b.png" />
 </div>
 </div>
 </section>
@@ -596,7 +596,7 @@ <h2>Example 3<a class="headerlink" href="#example-3" title="Permalink to this he
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/be07c30fb36dec62e097c15d9f147389f1c73bf98ae0f6713df75f14c9c42c24.png" src="../_images/be07c30fb36dec62e097c15d9f147389f1c73bf98ae0f6713df75f14c9c42c24.png" />
+<img alt="../_images/d626bc8f20d529cbdcebf5fc719e1121722e2867b111f9416301d294aab9971c.png" src="../_images/d626bc8f20d529cbdcebf5fc719e1121722e2867b111f9416301d294aab9971c.png" />
 </div>
 </div>
 <p>The cubic spline is not sensitive to small changes in the <span class="math notranslate nohighlight">\(y\)</span> values.</p>

diff --git a/notebooks/06_least_squares_regression.html b/notebooks/06_least_squares_regression.html
@@ -515,7 +515,7 @@ <h3>Example: Fake Noisy Linear Data<a class="headerlink" href="#example-fake-noi
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/08bf16d0256fd00d974981c82262b1a64a1b49b81242f48c85e9723bef01aa63.png" src="../_images/08bf16d0256fd00d974981c82262b1a64a1b49b81242f48c85e9723bef01aa63.png" />
+<img alt="../_images/f2bd79cd55d4411f9fae6e4a734a1641613b71e06b9357f77bfb516cc855f9fa.png" src="../_images/f2bd79cd55d4411f9fae6e4a734a1641613b71e06b9357f77bfb516cc855f9fa.png" />
 </div>
 </div>
 <p>Let’s use linear regression to retrieve the coefficients <span class="math notranslate nohighlight">\(c_0\)</span> and <span class="math notranslate nohighlight">\(c_1\)</span>. Construct the matrix <span class="math notranslate nohighlight">\(A\)</span>:</p>
@@ -540,11 +540,11 @@ <h3>Example: Fake Noisy Linear Data<a class="headerlink" href="#example-fake-noi
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[1.        , 0.10130289],
-       [1.        , 0.22893098],
-       [1.        , 0.90785401],
-       [1.        , 0.8108834 ],
-       [1.        , 0.1710018 ]])
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[1.        , 0.58343377],
+       [1.        , 0.75391049],
+       [1.        , 0.80513638],
+       [1.        , 0.93628357],
+       [1.        , 0.85067217]])
 </pre></div>
 </div>
 </div>
@@ -558,7 +558,7 @@ <h3>Example: Fake Noisy Linear Data<a class="headerlink" href="#example-fake-noi
 </div>
 </div>
 <div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 6.99569694 -4.00073615]
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[ 6.95249672 -3.90471848]
 </pre></div>
 </div>
 </div>
@@ -575,7 +575,7 @@ <h3>Example: Fake Noisy Linear Data<a class="headerlink" href="#example-fake-noi
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/b3bb4cb64631a51c9de1b8211c32ef9f4ba0367b81f02b960574abc663441bc6.png" src="../_images/b3bb4cb64631a51c9de1b8211c32ef9f4ba0367b81f02b960574abc663441bc6.png" />
+<img alt="../_images/975c3a72088dd27d21d8d35eeef41e1343eb97c272bf30f1f121395bfd2dd511.png" src="../_images/975c3a72088dd27d21d8d35eeef41e1343eb97c272bf30f1f121395bfd2dd511.png" />
 </div>
 </div>
 </section>
@@ -643,7 +643,7 @@ <h3>Example: Fake Noisy Quadratic Data<a class="headerlink" href="#example-fake-
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/78abacec3b487e958d2ea2afa313dc943b288bf8c0af650d9a1ab136c17ebe6e.png" src="../_images/78abacec3b487e958d2ea2afa313dc943b288bf8c0af650d9a1ab136c17ebe6e.png" />
+<img alt="../_images/b68652521a8b4da67d5745351536e402bdaed4a488e228f9c7f081726969cd65.png" src="../_images/b68652521a8b4da67d5745351536e402bdaed4a488e228f9c7f081726969cd65.png" />
 </div>
 </div>
 <p>Construct the matrix <span class="math notranslate nohighlight">\(A\)</span>:</p>
@@ -674,7 +674,7 @@ <h3>Example: Fake Noisy Quadratic Data<a class="headerlink" href="#example-fake-
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/98583080a1bfdda8593d1c60057fbded515efebacc14737567fd85f59e87f31d.png" src="../_images/98583080a1bfdda8593d1c60057fbded515efebacc14737567fd85f59e87f31d.png" />
+<img alt="../_images/ad0a50622eb742a99bf52ab0594ef0c59e9efc146361c0ee76191b085f0d1ad5.png" src="../_images/ad0a50622eb742a99bf52ab0594ef0c59e9efc146361c0ee76191b085f0d1ad5.png" />
 </div>
 </div>
 <p>Let’s solve again but this time we use the QR decomposition:</p>
@@ -699,7 +699,7 @@ <h3>Example: Fake Noisy Quadratic Data<a class="headerlink" href="#example-fake-
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/98583080a1bfdda8593d1c60057fbded515efebacc14737567fd85f59e87f31d.png" src="../_images/98583080a1bfdda8593d1c60057fbded515efebacc14737567fd85f59e87f31d.png" />
+<img alt="../_images/ad0a50622eb742a99bf52ab0594ef0c59e9efc146361c0ee76191b085f0d1ad5.png" src="../_images/ad0a50622eb742a99bf52ab0594ef0c59e9efc146361c0ee76191b085f0d1ad5.png" />
 </div>
 </div>
 </section>