a2 is now live

_config.yml

@@ -20,4 +20,4 @@ kramdown:
 
 # links to homeworks
 hw_1_colab: https://cs231n.github.io/assignments/2021/assignment1_colab.zip
-hw_2_colab:
+hw_2_colab: https://cs231n.github.io/assignments/2021/assignment2_colab.zip

assignments/2021/assignment2.md

@@ -58,12 +58,14 @@ In notebook `BatchNormalization.ipynb` you will implement batch normalization, a
 The notebook `Dropout.ipynb` will help you implement Dropout and explore its effects on model generalization.
 
 ### Q4: Convolutional Networks
+
 In the IPython Notebook `ConvolutionalNetworks.ipynb` you will implement several new layers that are commonly used in convolutional networks.
 
 ### Q5: PyTorch / TensorFlow on CIFAR-10
+
 For this last part, you will be working in either TensorFlow or PyTorch, two popular and powerful deep learning frameworks. **You only need to complete ONE of these two notebooks.** You do NOT need to do both, and we will _not_ be awarding extra credit to those who do.
 
-Open up either `PyTorch.ipynb` or `TensorFlow.ipynb`. There, you will learn how the framework works, culminating in training a  convolutional network of your own design on CIFAR-10 to get the best performance you can.
+Open up either `PyTorch.ipynb` or `TensorFlow.ipynb`. There, you will learn how the framework works, culminating in training a convolutional network of your own design on CIFAR-10 to get the best performance you can.
 
 ### Submitting your work
 

index.html

@@ -17,9 +17,9 @@
       <div class="materials-item">
         <a href="assignments2021/assignment1/">Assignment #1: Image Classification, kNN, SVM, Softmax, Fully-Connected Neural Network</a>
       </div>
-      <!-- <div class="materials-item">
+      <div class="materials-item">
         <a href="assignments2021/assignment2/">Assignment #2: Fully-Connected and Convolutional Nets, Batch Normalization, Dropout</a>
-      </div> -->
+      </div>
   </div>
 <!--
     <div class="materials-item">