Update documentation

_sources/lecture/cnn.md

@@ -167,7 +167,7 @@ width: 450px
 align: center
 name: cnn_multichannel
 ---
-Convolution of a 2-channel input with two kernels (Source: {cite}`zhang2021`, [here](https://d2l.ai/chapter_convolutional-neural-networks/channels.html))
+Convolution of a 2-channel input with a 2x2 kernel (Source: {cite}`zhang2021`, [here](https://d2l.ai/chapter_convolutional-neural-networks/channels.html))
 ```
 
 

exercise/bayes.html

@@ -664,8 +664,8 @@ <h3><span class="section-number">2.1.4. </span>Bayesian Linear Regression Model<
 <p>The model below essentially makes the following prior assumptions:</p>
 <div class="math notranslate nohighlight">
 \[y \approx h(x) = wx + b + \epsilon, \quad \text{with:}\]</div>
-<div class="amsmath math notranslate nohighlight" id="equation-e7d92532-7b7e-4bbf-882c-9a4b8823df5f">
-<span class="eqno">(2.28)<a class="headerlink" href="#equation-e7d92532-7b7e-4bbf-882c-9a4b8823df5f" title="Permalink to this equation">#</a></span>\[\begin{align}
+<div class="amsmath math notranslate nohighlight" id="equation-1cf407bf-e781-41c5-a840-55be83a579ce">
+<span class="eqno">(2.28)<a class="headerlink" href="#equation-1cf407bf-e781-41c5-a840-55be83a579ce" title="Permalink to this equation">#</a></span>\[\begin{align}
 y_i &amp;\sim \mathcal{N}(\mu, \sigma^2)\\
 \mu &amp;= w \cdot x_i + b\\
 w &amp;\sim \mathcal{N}(0,1^2)\\

lecture/cnn.html

@@ -569,7 +569,7 @@ <h3><span class="section-number">10.4.1. </span>Channels<a class="headerlink" hr
 <figure class="align-center" id="cnn-multichannel">
 <a class="reference internal image-reference" href="../_images/cnn_multichannel.png"><img alt="../_images/cnn_multichannel.png" src="../_images/cnn_multichannel.png" style="width: 450px;" /></a>
 <figcaption>
-<p><span class="caption-number">Fig. 10.10 </span><span class="caption-text">Convolution of a 2-channel input with two kernels (Source: <span id="id7">[<a class="reference internal" href="../references.html#id12" title="Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into deep learning. arXiv preprint arXiv:2106.11342, 2021. URL: https://d2l.ai/.">Zhang <em>et al.</em>, 2021</a>]</span>, <a class="reference external" href="https://d2l.ai/chapter_convolutional-neural-networks/channels.html">here</a>)</span><a class="headerlink" href="#cnn-multichannel" title="Permalink to this image">#</a></p>
+<p><span class="caption-number">Fig. 10.10 </span><span class="caption-text">Convolution of a 2-channel input with a 2x2 kernel (Source: <span id="id7">[<a class="reference internal" href="../references.html#id12" title="Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into deep learning. arXiv preprint arXiv:2106.11342, 2021. URL: https://d2l.ai/.">Zhang <em>et al.</em>, 2021</a>]</span>, <a class="reference external" href="https://d2l.ai/chapter_convolutional-neural-networks/channels.html">here</a>)</span><a class="headerlink" href="#cnn-multichannel" title="Permalink to this image">#</a></p>
 </figcaption>
 </figure>
 </section>