diff --git a/_modules/week-01.md b/_modules/week-01.md
index 7bdfbc9..e732206 100644
--- a/_modules/week-01.md
+++ b/_modules/week-01.md
@@ -9,8 +9,12 @@ Jan 7
   : [Slides](assets/slides/jan7.pdf) • [Recording]()
 : **Survey**{: .label .label-survey} [Beginning of Quarter Survey](https://forms.gle/4fuE1HUFbd13NKbp7) **(Due: End of Week 2 - 1/19)**
 : **Readings**{: .label .label-reading} **(Due 1/14)**
-  * **Required**: [1.1 - MLSys : Intro](https://mlsysbook.ai/contents/core/introduction/introduction.html), [1.2 - DNN](https://mlsysbook.ai/contents/core/dnn_architectures/dnn_architectures.html#sec-deep-learning-primer-resource)
-  * **Optional**: [1.3 - Petuum](https://arxiv.org/abs/1312.7651), [1.4 - Systems Challenges for AI](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-159.pdf)
+  * **Required**: 
+    * [1.1 - MLSys : Intro](https://mlsysbook.ai/contents/core/introduction/introduction.html)
+    * [1.2 - DNN](https://mlsysbook.ai/contents/core/dnn_architectures/dnn_architectures.html#sec-deep-learning-primer-resource)
+  * **Optional**: 
+    * [1.3 - Petuum](https://arxiv.org/abs/1312.7651)
+    * [1.4 - Systems Challenges for AI](https://www2.eecs.berkeley.edu/Pubs/TechRpts/2017/EECS-2017-159.pdf)
 
 
 
@@ -29,7 +33,8 @@ Jan 14
     * [2.2 - PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://arxiv.org/abs/1912.01703)
     * [2.3 - Automatic Differentiation in Machine Learning: a Survey (Page 1 - 14 (Chapter 1 - 3)](https://arxiv.org/pdf/1502.05767)
   * **Optional**: 
-    * 2.4 - Pytorch autodiff blogs: [(a) Overview of PyTorch Autograd Engine](https://pytorch.org/blog/overview-of-pytorch-autograd-engine/) [(b) How Computational Graphs are Constructed in PyTorch](https://pytorch.org/blog/computational-graphs-constructed-in-pytorch/)
+    * [2.4(a) - Pytorch autodiff blogs (a): Overview of PyTorch Autograd Engine](https://pytorch.org/blog/overview-of-pytorch-autograd-engine/)
+    * [2.4(b) - Pytorch autodiff blogs (b): How Computational Graphs are Constructed in PyTorch](https://pytorch.org/blog/computational-graphs-constructed-in-pytorch/)
     * [2.5 - DyNet: The Dynamic Neural Network Toolkit](https://arxiv.org/pdf/1701.03980)
 
 <!-- : **Readings**{: .label .label-reading} -->