prep for assignment2

Author: kevinzakka

_config.yml

@@ -20,3 +20,4 @@ kramdown:
 
 # links to homeworks
 hw_1_colab: https://cs231n.github.io/assignments/2021/assignment1_colab.zip
+hw_2_colab:

assignments/2021/assignment2.md

@@ -0,0 +1,87 @@
+---
+layout: page
+title: Assignment 2
+mathjax: true
+permalink: /assignments2021/assignment2/
+---
+
+<span style="color:red">This assignment is due on **Friday, April 30 2021** at 11:59pm PST.</span>
+
+Starter code containing Colab notebooks can be [downloaded here]({{site.hw_2_colab}}).
+
+- [Setup](#setup)
+- [Goals](#goals)
+- [Q1: Fully-connected Neural Network](#q1-fully-connected-neural-network)
+- [Q2: Batch Normalization](#q2-batch-normalization)
+- [Q3: Dropout](#q3-dropout)
+- [Q4: Convolutional Networks](#q4-convolutional-networks)
+- [Q5: PyTorch / TensorFlow on CIFAR-10](#q5-pytorch--tensorflow-on-cifar-10)
+- [Submitting your work](#submitting-your-work)
+
+### Setup
+
+Please familiarize yourself with the [recommended workflow]({{site.baseurl}}/setup-instructions/#working-remotely-on-google-colaboratory) before starting the assignment. You should also watch the Colab walkthrough tutorial below.
+
+<iframe style="display: block; margin: auto;" width="560" height="315" src="https://www.youtube.com/embed/IZUz4pRYlus" frameborder="0" allowfullscreen></iframe>
+
+**Note**. Ensure you are periodically saving your notebook (`File -> Save`) so that you don't lose your progress if you step away from the assignment and the Colab VM disconnects.
+
+While we don't officially support local development, we've added a <b>requirements.txt</b> file that you can use to setup a virtual env.
+
+Once you have completed all Colab notebooks **except `collect_submission.ipynb`**, proceed to the [submission instructions](#submitting-your-work).
+
+### Goals
+
+In this assignment you will practice writing backpropagation code, and training Neural Networks and Convolutional Neural Networks. The goals of this assignment are as follows:
+
+- Understand **Neural Networks** and how they are arranged in layered architectures.
+- Understand and be able to implement (vectorized) **backpropagation**.
+- Implement various **update rules** used to optimize Neural Networks.
+- Implement **Batch Normalization** and **Layer Normalization** for training deep networks.
+- Implement **Dropout** to regularize networks.
+- Understand the architecture of **Convolutional Neural Networks** and get practice with training them.
+- Gain experience with a major deep learning framework, such as **TensorFlow** or **PyTorch**.
+
+### Q1: Fully-connected Neural Network
+
+The notebook `FullyConnectedNets.ipynb` will introduce you to our
+modular layer design, and then use those layers to implement fully-connected
+networks of arbitrary depth. To optimize these models you will implement several
+popular update rules.
+
+### Q2: Batch Normalization
+
+In notebook `BatchNormalization.ipynb` you will implement batch normalization, and use it to train deep fully-connected networks.
+
+### Q3: Dropout
+
+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.
+
+### Submitting your work
+
+**Important**. Please make sure that the submitted notebooks have been run and the cell outputs are visible.
+
+Once you have completed all notebooks and filled out the necessary code, you need to follow the below instructions to submit your work:
+
+**1.** Open `collect_submission.ipynb` in Colab and execute the notebook cells.
+
+This notebook/script will:
+
+* Generate a zip file of your code (`.py` and `.ipynb`) called `a2.zip`.
+* Convert all notebooks into a single PDF file.
+
+If your submission for this step was successful, you should see the following display message:
+
+`### Done! Please submit a1.zip and the pdfs to Gradescope. ###`
+
+**2.** Submit the PDF and the zip file to [Gradescope](https://www.gradescope.com/courses/257661).
+
+Remember to download `a2.zip` and `assignment.pdf` locally before submitting to Gradescope.

index.html

@@ -17,6 +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">
+        <a href="assignments2021/assignment2/">Assignment #2: Fully-Connected and Convolutional Nets, Batch Normalization, Dropout</a>
+      </div> -->
   </div>
 <!--
     <div class="materials-item">