added assignment2 skeleton

Author: kevinzakka

_config.yml

@@ -21,3 +21,5 @@ kramdown:
 # links to homeworks
 hw_1_jupyter: https://cs231n.github.io/assignments/2020/assignment1_jupyter.zip
 hw_1_colab: https://cs231n.github.io/assignments/2020/assignment1_colab.zip
+hw_2_jupyter:
+hw_2_colab:

assignments/2020/assignment2.md

@@ -0,0 +1,121 @@
+---
+layout: page
+title: Assignment 2
+mathjax: true
+permalink: /assignments2020/assignment2/
+---
+
+This assignment is due on **Wednesday, May 6 2020** at 11:59pm PST.
+
+<details>
+<summary>Handy Download Links</summary>
+
+ <ul>
+  <li><a href="{{ site.hw_2_colab }}">Option A: Colab starter code</a></li>
+  <li><a href="{{ site.hw_2_jupyter }}">Option B: Jupyter starter code</a></li>
+</ul>
+</details>
+
+- [Goals](#goals)
+- [Setup](#setup)
+  - [Option A: Google Colaboratory (Recommended)](#option-a-google-colaboratory-recommended)
+  - [Option B: Local Development](#option-b-local-development)
+- [Q1: Fully-connected Neural Network (20 points)](#q1-fully-connected-neural-network-20-points)
+- [Q2: Batch Normalization (30 points)](#q2-batch-normalization-30-points)
+- [Q3: Dropout (10 points)](#q3-dropout-10-points)
+- [Q4: Convolutional Networks (30 points)](#q4-convolutional-networks-30-points)
+- [Q5: PyTorch / TensorFlow on CIFAR-10 (10 points)](#q5-pytorch--tensorflow-on-cifar-10-10-points)
+- [Submitting your work](#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 these models on data.
+- Gain experience with a major deep learning framework, such as **TensorFlow** or **PyTorch**.
+
+### Setup
+
+You can work on the assignment in one of two ways: **remotely** on Google Colaboratory or **locally** on your own machine.
+
+**Regardless of the method chosen, ensure you have followed the [setup instructions](/setup-instructions) before proceeding.**
+
+#### Option A: Google Colaboratory (Recommended)
+
+**Download.** Starter code containing Colab notebooks can be downloaded [here]({{site.hw_1_colab}}).
+
+If you choose to work with Google Colab, please familiarize yourself with the [recommended workflow]({{site.baseurl}}/setup-instructions/#working-remotely-on-google-colaboratory).
+
+**Note 1**. Please make sure that you work on the Colab notebooks in the order of the questions (see below). The reason is that the code cells that get executed *at the end* of the notebooks save the modified files back to your drive and some notebooks may require code from previous notebook.
+
+**Note 2**. Related to above, ensure you are periodically saving your notebook (`File -> Save`), and any edited `.py` files relevant to that notebook (i.e. **by executing the last code cell**) so that you don't lose your progress if you step away from the assignment and the Colab VM disconnects.
+
+Once you have completed all Colab notebooks **except `collect_submission.ipynb`**, proceed to the [submission instructions](#submitting-your-work).
+
+#### Option B: Local Development
+
+**Download.** Starter code containing jupyter notebooks can be downloaded [here]({{site.hw_1_jupyter}}).
+
+**Install Packages**. Once you have the starter code, activate your environment (the one you installed in the [Software Setup]({{site.baseurl}}/setup-instructions/) page) and run `pip install -r requirements.txt`.
+
+**Download CIFAR-10**. Next, you will need to download the CIFAR-10 dataset. Run the following from the `assignment2` directory:
+
+```bash
+cd cs231n/datasets
+./get_datasets.sh
+```
+**Start Jupyter Server**. After you have the CIFAR-10 data, you should start the Jupyter server from the
+`assignment1` directory by executing `jupyter notebook` in your terminal.
+
+Complete each notebook, then once you are done, go to the [submission instructions](#submitting-your-work).
+
+### Q1: Fully-connected Neural Network (20 points)
+
+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 (30 points)
+
+In notebook `BatchNormalization.ipynb` you will implement batch normalization, and use it to train deep fully-connected networks.
+
+### Q3: Dropout (10 points)
+
+The notebook `Dropout.ipynb` will help you implement Dropout and explore its effects on model generalization.
+
+### Q4: Convolutional Networks (30 points)
+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 (10 points)
+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, there are **_two_** steps you must follow to submit your assignment:
+
+**1.** If you selected Option A and worked on the assignment in Colab, open `collect_submission.ipynb` in Colab and execute the notebook cells. If you selected Option B and worked on the assignment locally, run the bash script in `assignment2` by executing `bash collectSubmission.sh`.
+
+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.
+
+**Note for Option B users**. You must have (a) `nbconvert` installed with Pandoc and Tex support and (b) `PyPDF2` installed to successfully convert your notebooks to a PDF file. Please follow these [installation instructions](https://nbconvert.readthedocs.io/en/latest/install.html#installing-nbconvert) to install (a) and run `pip install PyPDF2` to install (b). If you are, for some inexplicable reason, unable to successfully install the above dependencies, you can manually convert each jupyter notebook to HTML (`File -> Download as -> HTML (.html)`), save the HTML page as a PDF, then concatenate all the PDFs into a single PDF submission using your favorite PDF viewer.
+
+If your submission for this step was successful, you should see the following display message:
+
+`### Done! Please submit a2.zip and the pdfs to Gradescope. ###`
+
+**2.** Submit the PDF and the zip file to [Gradescope](https://www.gradescope.com/courses/103764).
+
+**Note for Option A users**. Remember to download `a2.zip` and `assignment.pdf` locally before submitting to Gradescope.