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diff --git a/README.html b/README.html
@@ -194,16 +194,15 @@
 
 
 <li class="toctree-l1"><a class="reference internal" href="notebooks/Tutorial%204%20-%20Decision%20Trees.html">Recap: Decision Trees</a></li>
-
-
 <li class="toctree-l1"><a class="reference internal" href="notebooks/Tutorial%205%20-%20Nearest%20Neighbors.html">Recap: k-Nearest Neighbor</a></li>
 <li class="toctree-l1"><a class="reference internal" href="labs/Lab%201%20-%20Tutorial.html">Lab 1: Machine Learning with Python</a></li>
 
 
 
-<li class="toctree-l1"><a class="reference internal" href="labs/Lab%204%20-%20Tutorial.html">Lab 4 Tutorial: Data engineering pipelines</a></li>
-<li class="toctree-l1"><a class="reference internal" href="labs/Lab%206%20-%20Tutorial.html">Lab 6 Tutorial: Deep Learning with TensorFlow</a></li>
-<li class="toctree-l1"><a class="reference internal" href="labs/Lab%207%20-%20Tutorial.html">Lab 7 Tutorial: Deep Learning for text</a></li>
+<li class="toctree-l1"><a class="reference internal" href="labs/Lab%202%20-%20Tutorial.html">Lab 2: Model Selection in scikit-learn</a></li>
+<li class="toctree-l1"><a class="reference internal" href="labs/Lab%204%20-%20Tutorial.html">Lab 4: Data engineering pipelines</a></li>
+<li class="toctree-l1"><a class="reference internal" href="labs/Lab%206%20-%20Tutorial.html">Lab 6: Deep Learning with TensorFlow</a></li>
+<li class="toctree-l1"><a class="reference internal" href="labs/Lab%207%20-%20Tutorial.html">Lab 7: Deep Learning for text</a></li>
 </ul>
 
     </div>

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diff --git a/_sources/labs/Lab 0 - Prerequisites.ipynb b/_sources/labs/Lab 0 - Prerequisites.ipynb
@@ -41,15 +41,14 @@
     "**All**: Install the correct version of [graphviz](https://graphviz.org/download/) according to your OS.  \n",
     "\n",
     "#### Apple silicon (M1/M2)\n",
-    "For those who have a laptop with Apple Silicon (M1), [this guide may be useful](https://medium.com/mlearning-ai/install-tensorflow-on-mac-m1-m2-with-gpu-support-c404c6cfb580) to install a TensorFlow version that will effectively use the GPUs. However, installing the latest version of tensorflow-macos doesn't always work reliably and you may have to downgrade to an older version.\n",
+    "For those who have a laptop with Apple Silicon (M1), [this guide may be useful](https://developer.apple.com/metal/tensorflow-plugin/) to install a TensorFlow version that will effectively use the GPUs.\n",
     "\n",
     "This procedure has been known to work using Miniconda3 and Python 3.10 on M1 chips:\n",
     "```\n",
     "cd to your miniconda directory\n",
-    "conda install -c apple tensorflow-deps\n",
     "conda install -c conda-forge cvxpy\n",
-    "pip install \"tensorflow-macos==2.10\"\n",
-    "pip install \"tensorflow-metal==0.6\"\n",
+    "pip install \"tensorflow\"\n",
+    "pip install \"tensorflow-metal\"\n",
     "```\n",
     "\n",
     "The conda install of `cvxpy` is to resolve issues with libraries with poor M1 support (e.g. `fancyimpute`)  "

diff --git a/_sources/labs/Lab 1a - Linear Models for Regression Solution.ipynb b/_sources/labs/Lab 1a - Linear Models for Regression Solution.ipynb
diff --git a/_sources/notebooks/02 - Linear Models.ipynb b/_sources/notebooks/02 - Linear Models.ipynb
diff --git a/_sources/notebooks/03 - Kernelization.ipynb b/_sources/notebooks/03 - Kernelization.ipynb
diff --git a/_sources/notebooks/04 - Model Selection.ipynb b/_sources/notebooks/04 - Model Selection.ipynb
diff --git a/_sources/notebooks/08 - Neural Networks.ipynb b/_sources/notebooks/08 - Neural Networks.ipynb
@@ -2821,7 +2821,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.10"
   }
  },
  "nbformat": 4,

diff --git a/_sources/notebooks/09 - Convolutional Neural Networks.ipynb b/_sources/notebooks/09 - Convolutional Neural Networks.ipynb
@@ -5034,7 +5034,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.10"
   },
   "rise": {
    "theme": "white",

diff --git a/labs/Lab 0 - Prerequisites.html b/labs/Lab 0 - Prerequisites.html
@@ -196,16 +196,15 @@
 
 
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%204%20-%20Decision%20Trees.html">Recap: Decision Trees</a></li>
-
-
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%205%20-%20Nearest%20Neighbors.html">Recap: k-Nearest Neighbor</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Lab%201%20-%20Tutorial.html">Lab 1: Machine Learning with Python</a></li>
 
 
 
-<li class="toctree-l1"><a class="reference internal" href="Lab%204%20-%20Tutorial.html">Lab 4 Tutorial: Data engineering pipelines</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Lab%206%20-%20Tutorial.html">Lab 6 Tutorial: Deep Learning with TensorFlow</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Lab%207%20-%20Tutorial.html">Lab 7 Tutorial: Deep Learning for text</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%202%20-%20Tutorial.html">Lab 2: Model Selection in scikit-learn</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%204%20-%20Tutorial.html">Lab 4: Data engineering pipelines</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%206%20-%20Tutorial.html">Lab 6: Deep Learning with TensorFlow</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%207%20-%20Tutorial.html">Lab 7: Deep Learning for text</a></li>
 </ul>
 
     </div>
@@ -475,13 +474,12 @@ <h3>OS specific notes<a class="headerlink" href="#os-specific-notes" title="Perm
 <p><strong>All</strong>: Install the correct version of <a class="reference external" href="https://graphviz.org/download/">graphviz</a> according to your OS.</p>
 <section id="apple-silicon-m1-m2">
 <h4>Apple silicon (M1/M2)<a class="headerlink" href="#apple-silicon-m1-m2" title="Permalink to this heading">#</a></h4>
-<p>For those who have a laptop with Apple Silicon (M1), <a class="reference external" href="https://medium.com/mlearning-ai/install-tensorflow-on-mac-m1-m2-with-gpu-support-c404c6cfb580">this guide may be useful</a> to install a TensorFlow version that will effectively use the GPUs. However, installing the latest version of tensorflow-macos doesn’t always work reliably and you may have to downgrade to an older version.</p>
+<p>For those who have a laptop with Apple Silicon (M1), <a class="reference external" href="https://developer.apple.com/metal/tensorflow-plugin/">this guide may be useful</a> to install a TensorFlow version that will effectively use the GPUs.</p>
 <p>This procedure has been known to work using Miniconda3 and Python 3.10 on M1 chips:</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cd</span> <span class="n">to</span> <span class="n">your</span> <span class="n">miniconda</span> <span class="n">directory</span>
-<span class="n">conda</span> <span class="n">install</span> <span class="o">-</span><span class="n">c</span> <span class="n">apple</span> <span class="n">tensorflow</span><span class="o">-</span><span class="n">deps</span>
 <span class="n">conda</span> <span class="n">install</span> <span class="o">-</span><span class="n">c</span> <span class="n">conda</span><span class="o">-</span><span class="n">forge</span> <span class="n">cvxpy</span>
-<span class="n">pip</span> <span class="n">install</span> <span class="s2">&quot;tensorflow-macos==2.10&quot;</span>
-<span class="n">pip</span> <span class="n">install</span> <span class="s2">&quot;tensorflow-metal==0.6&quot;</span>
+<span class="n">pip</span> <span class="n">install</span> <span class="s2">&quot;tensorflow&quot;</span>
+<span class="n">pip</span> <span class="n">install</span> <span class="s2">&quot;tensorflow-metal&quot;</span>
 </pre></div>
 </div>
 <p>The conda install of <code class="docutils literal notranslate"><span class="pre">cvxpy</span></code> is to resolve issues with libraries with poor M1 support (e.g. <code class="docutils literal notranslate"><span class="pre">fancyimpute</span></code>)</p>

diff --git a/labs/Lab 1 - Tutorial.html b/labs/Lab 1 - Tutorial.html
@@ -196,8 +196,6 @@
 
 
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%204%20-%20Decision%20Trees.html">Recap: Decision Trees</a></li>
-
-
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%205%20-%20Nearest%20Neighbors.html">Recap: k-Nearest Neighbor</a></li>
 <li class="toctree-l1 current active"><a class="current reference internal" href="#">Lab 1: Machine Learning with Python</a></li>
 

diff --git a/labs/Lab 1a - Linear Models for Regression Solution.html b/labs/Lab 1a - Linear Models for Regression Solution.html
@@ -542,7 +542,7 @@ <h2>Quick visualization<a class="headerlink" href="#quick-visualization" title="
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/8c48c118634ac5f737e78ecd36a28e0be31c516ed9ea21f14506af16d942d418.png" src="../_images/8c48c118634ac5f737e78ecd36a28e0be31c516ed9ea21f14506af16d942d418.png" />
+<img alt="../_images/baf1ffe021a7b10a78e9ac4c8104acc616ce13468fe19b072bf4eb609909d789.png" src="../_images/baf1ffe021a7b10a78e9ac4c8104acc616ce13468fe19b072bf4eb609909d789.png" />
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@@ -642,7 +642,7 @@ <h2>Quick visualization<a class="headerlink" href="#quick-visualization" title="
 </div>
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 <div class="cell_output docutils container">
-<img alt="../_images/0809975f925ab0f01c8b205b50e714a69994daee598e8a8e96677dc46a06ca01.png" src="../_images/0809975f925ab0f01c8b205b50e714a69994daee598e8a8e96677dc46a06ca01.png" />
+<img alt="../_images/90a37d3781e5dc48723ef4b924889e45c70de88c6eb2fa9e41af06926628c01e.png" src="../_images/90a37d3781e5dc48723ef4b924889e45c70de88c6eb2fa9e41af06926628c01e.png" />
 </div>
 </div>
 <p>We can see that the target (<span class="math notranslate nohighlight">\(NO_2\)</span> levels) seem to be correlated to the number of cars per hour, which makes sense because cars produce <span class="math notranslate nohighlight">\(NO_2\)</span>. Other influences (air temperature differences and wind) seem to have a more complex and subtle effect. Let’s try to model these using linear regression models.</p>
@@ -756,7 +756,7 @@ <h4>Solution<a class="headerlink" href="#id1" title="Permalink to this heading">
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/781c74b09bb62245202394cc3f7edab634155e2ff0fab05676f2bcc1d1b5bb8e.png" src="../_images/781c74b09bb62245202394cc3f7edab634155e2ff0fab05676f2bcc1d1b5bb8e.png" />
+<img alt="../_images/6bec410d05ca059f164bcfe728ca019d233300cdb29aee172193f4ebb660f0f9.png" src="../_images/6bec410d05ca059f164bcfe728ca019d233300cdb29aee172193f4ebb660f0f9.png" />
 </div>
 </div>
 </section>
@@ -773,7 +773,7 @@ <h4>Solution<a class="headerlink" href="#id2" title="Permalink to this heading">
 <section id="exercise-2-regularization">
 <h2>Exercise 2: Regularization<a class="headerlink" href="#exercise-2-regularization" title="Permalink to this heading">#</a></h2>
 <p>We will now tune these algorithm’s main regularization hyperparameter: the regularization hyperparameter (<code class="docutils literal notranslate"><span class="pre">alpha</span></code>) in Lasso and Ridge, and the number of neighbors (<code class="docutils literal notranslate"><span class="pre">n_neighbors</span></code>) in kNN.</p>
-<p>We expect the optimum for the alpha parameters to lie in <span class="math notranslate nohighlight">\([10^{-12},10^{12}]\)</span> and for n_neighbors between 1 and 50. alpha should be varied on a log scale (i.e. [0.01, 0.1, 1, 10, 100]), k should be varied uniformly (i.e. [1,2,3,4]).</p>
+<p>We expect the optimum for the alpha parameters to lie in <span class="math notranslate nohighlight">\([10^{-12},10^{12}]\)</span> and for n_neighbors between 1 and 60. alpha should be varied on a log scale (i.e. [0.01, 0.1, 1, 10, 100]), k should be varied uniformly or geometrically.</p>
 <section id="exercise-2-1">
 <h3>Exercise 2.1<a class="headerlink" href="#exercise-2-1" title="Permalink to this heading">#</a></h3>
 <p>Vary the hyperparameters in the range given above and, for each regressor, create a line plot that plots both the training and test score for every value of the regularization hyperparameter. Hence, you should produce 3 plots, one for each regressor. Use the default 5-fold cross validation for all scores, but only plot the means.</p>
@@ -835,7 +835,7 @@ <h4>Solution<a class="headerlink" href="#id3" title="Permalink to this heading">
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/e9097d5a7dbf4c2f163940b8a53cd86dd5b8094fc3f6019c237c6d28728f3781.png" src="../_images/e9097d5a7dbf4c2f163940b8a53cd86dd5b8094fc3f6019c237c6d28728f3781.png" />
+<img alt="../_images/c83ea8b4a0198a426c940519e813041c19d9306ba694ce6d6870e0e4b326a325.png" src="../_images/c83ea8b4a0198a426c940519e813041c19d9306ba694ce6d6870e0e4b326a325.png" />
 </div>
 </div>
 </section>
@@ -912,7 +912,7 @@ <h4>Solution<a class="headerlink" href="#id5" title="Permalink to this heading">
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/e154775f48abf02a54e1f4c4ae85e99db588736b7150af1cf743b47d0cd9367f.png" src="../_images/e154775f48abf02a54e1f4c4ae85e99db588736b7150af1cf743b47d0cd9367f.png" />
+<img alt="../_images/ae000fa3c50d046c5e07b8aef2da788017564ad6102ec47913378fcf24d85c2f.png" src="../_images/ae000fa3c50d046c5e07b8aef2da788017564ad6102ec47913378fcf24d85c2f.png" />
 </div>
 </div>
 <p>For ElasticNet we see the same sudden drop in performance around alpha=0.1. For l1_ratio=0, it is identical to Ridge (L2), showing a more gradual descent. For l1_ration=1, it is identical to Lasso (L1), showing the same sharp performance drop.</p>
@@ -946,7 +946,7 @@ <h4>Solution<a class="headerlink" href="#id6" title="Permalink to this heading">
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/3eae0847f567c3e0d9a63422cc8dc1e08403db76ec4296c68ee7251a60951631.png" src="../_images/3eae0847f567c3e0d9a63422cc8dc1e08403db76ec4296c68ee7251a60951631.png" />
+<img alt="../_images/17fd5b82688b9f6b803dd8679b47dd28bfd45672d542e44b38619c31a5707a2d.png" src="../_images/17fd5b82688b9f6b803dd8679b47dd28bfd45672d542e44b38619c31a5707a2d.png" />
 </div>
 </div>
 <p>The different techniques find almost <em>exactly</em> the same coefficients (the markers overlap). cars_per_hour is the most influential, followed by temperature_diff_2m_25m and wind_speed. The others are nearly zero.</p>
@@ -965,7 +965,7 @@ <h4>Solution<a class="headerlink" href="#id7" title="Permalink to this heading">
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../_images/7c1a0a6e2f88bd4820741b80a1c1f6e0205292eb669c680a7e2a8841e0dbfe4e.png" src="../_images/7c1a0a6e2f88bd4820741b80a1c1f6e0205292eb669c680a7e2a8841e0dbfe4e.png" />
+<img alt="../_images/91e1eef3155a8a43912a52a31b8366542becdc3668b98c28f00ce53c7126a157.png" src="../_images/91e1eef3155a8a43912a52a31b8366542becdc3668b98c28f00ce53c7126a157.png" />
 </div>
 </div>
 </section>

diff --git a/labs/Lab 1a - Linear Models for Regression.html b/labs/Lab 1a - Linear Models for Regression.html
@@ -198,16 +198,15 @@
 
 
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%204%20-%20Decision%20Trees.html">Recap: Decision Trees</a></li>
-
-
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%205%20-%20Nearest%20Neighbors.html">Recap: k-Nearest Neighbor</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Lab%201%20-%20Tutorial.html">Lab 1: Machine Learning with Python</a></li>
 
 
 
-<li class="toctree-l1"><a class="reference internal" href="Lab%204%20-%20Tutorial.html">Lab 4 Tutorial: Data engineering pipelines</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Lab%206%20-%20Tutorial.html">Lab 6 Tutorial: Deep Learning with TensorFlow</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Lab%207%20-%20Tutorial.html">Lab 7 Tutorial: Deep Learning for text</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%202%20-%20Tutorial.html">Lab 2: Model Selection in scikit-learn</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%204%20-%20Tutorial.html">Lab 4: Data engineering pipelines</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%206%20-%20Tutorial.html">Lab 6: Deep Learning with TensorFlow</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%207%20-%20Tutorial.html">Lab 7: Deep Learning for text</a></li>
 </ul>
 
     </div>

diff --git a/labs/Lab 1b - Linear Models for Classification.html b/labs/Lab 1b - Linear Models for Classification.html
@@ -198,16 +198,15 @@
 
 
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%204%20-%20Decision%20Trees.html">Recap: Decision Trees</a></li>
-
-
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%205%20-%20Nearest%20Neighbors.html">Recap: k-Nearest Neighbor</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Lab%201%20-%20Tutorial.html">Lab 1: Machine Learning with Python</a></li>
 
 
 
-<li class="toctree-l1"><a class="reference internal" href="Lab%204%20-%20Tutorial.html">Lab 4 Tutorial: Data engineering pipelines</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Lab%206%20-%20Tutorial.html">Lab 6 Tutorial: Deep Learning with TensorFlow</a></li>
-<li class="toctree-l1"><a class="reference internal" href="Lab%207%20-%20Tutorial.html">Lab 7 Tutorial: Deep Learning for text</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%202%20-%20Tutorial.html">Lab 2: Model Selection in scikit-learn</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%204%20-%20Tutorial.html">Lab 4: Data engineering pipelines</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%206%20-%20Tutorial.html">Lab 6: Deep Learning with TensorFlow</a></li>
+<li class="toctree-l1"><a class="reference internal" href="Lab%207%20-%20Tutorial.html">Lab 7: Deep Learning for text</a></li>
 </ul>
 
     </div>

diff --git a/labs/Lab 2 - Tutorial.html b/labs/Lab 2 - Tutorial.html
@@ -196,8 +196,6 @@
 
 
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%204%20-%20Decision%20Trees.html">Recap: Decision Trees</a></li>
-
-
 <li class="toctree-l1"><a class="reference internal" href="../notebooks/Tutorial%205%20-%20Nearest%20Neighbors.html">Recap: k-Nearest Neighbor</a></li>
 <li class="toctree-l1"><a class="reference internal" href="Lab%201%20-%20Tutorial.html">Lab 1: Machine Learning with Python</a></li>