[ZH] Fixing some issue on 13-2 (#698)

Issues:
I forgot to comment out some English words.
The "[基準化分析法圖形神經網絡](https://arxiv.org/pdf/2003.00982.pdf/)" shows the code rather than the link and words.
I see ### and ** often do not work in Chinese. I will see what we will have after this pull request.

docs/zh/week13/13-2.md

@@ -115,9 +115,7 @@ $$
 <!-- Now we have stability under coefficient perturbation. -->
 現在我們就在系数扰动(coefficient perturbation)下有穩定性。
 
-ChebNets are GCNs that can be used for any arbitrary graph domain, but the limitation is that they are isotropic. Standard ConvNets produce *anisotropic* filters because Euclidean grids have direction, while Spectral GCNs compute *isotropic* filters since graphs have no notion of direction (up, down, left, right).
-
-<!-- We can extend ChebNets to multiple graphs using a 2D spectral filter. This may be useful, for example, in recommender systems where we have movie graphs and user graphs. Multi-graph ChebNets have the activation equation as below. -->
+<!--ChebNets are GCNs that can be used for any arbitrary graph domain, but the limitation is that they are isotropic. Standard ConvNets produce *anisotropic* filters because Euclidean grids have direction, while Spectral GCNs compute *isotropic* filters since graphs have no notion of direction (up, down, left, right). We can extend ChebNets to multiple graphs using a 2D spectral filter. This may be useful, for example, in recommender systems where we have movie graphs and user graphs. Multi-graph ChebNets have the activation equation as below. -->
 切布网路們都是圖形卷積網絡，它能夠用在任何任意的圖形領域上，但限制是它們都是各向同性的。標準的卷積网生成各向異性的過濾器，那是因為歐幾里得格子(Euclidean grids)是有方向的，同時光譜圖形卷積網絡計算各向同性過濾器，這是因為國形沒有方向這個概念，比如上下左右。我們可以延伸切佈網路到多重使用一個2d光譜過濾器的圖形。這或許是有用的，比如，在推薦系統中，我們有影片圖形和用戶圖形。多重圖形切佈網路有激活方程，下方所示。
 
 $$
@@ -243,7 +241,7 @@ So, the activation of the next layer $h_{i}^{l+1}$ is a function of the activati
 所以，下一层$h_{i}^{l+1}$的这个激活是...，就是在节点$i$和节点$i$的邻域的之前那一层$h_{i}^{l}$的激活的函数。当我们去改变这个函数时，我们就有整个图形的家族
 
 
-### ChebNets and Vanilla Spatial GCNs
+<!-- ### ChebNets and Vanilla Spatial GCNs -->
 ### 切布网络(ChebNets)和基础式空间性图形卷积网络(Vanilla Spatial GCNs)
 
 <!-- The above defined Vanilla Spatial GCN is a simplification of ChebNets. We can truncate the expansion of ChebNet by using the first two Chebyshev functions to end up with, -->
@@ -297,7 +295,7 @@ $$h_{i}^{l+1} = (1 + \epsilon)h_{i}^{l} + \sum_{j \in N_{i}} h_{j}^{l}$$
 <center>
 <img src="{{site.baseurl}}/images/week13/13-2/Figure5.png" /><br>
 <!-- <b>Figure 5:</b> Examples of two isomorphic graphs -->
-<b>图5:</b> : 两个同构图形的例子
+<b>图5:</b> 两个同构图形的例子
 
 
 <!-- ## [Anisotropic GCNs](https://www.youtube.com/watch?v=Iiv9R6BjxHM&list=PLLHTzKZzVU9eaEyErdV26ikyolxOsz6mq&index=24&t=5586s) -->