a2 instructions (without link)

Author: Winnie Lin

assignments/2019/assignment2.md

@@ -0,0 +1,74 @@
+---
+layout: page
+mathjax: true
+permalink: /assignments2019/assignment2/
+---
+
+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
+Get the code as a zip file [here].
+You can follow the setup instructions [here](/setup-instructions).
+
+If you've perform the google cloud setup already for assignment1, you can skip this step and use the virtual machine you created previously. 
+(However, if you're using your virtual machine from assignment1, you might need to perform additional installation steps for the 5th notebook depending on whether you're using Pytorch or Tensorflow. See below for details.)
+
+### Some Notes
+**NOTE 1:** This year, the `assignment2` code has been tested to be compatible with python version `3.7` (it may work with other versions of `3.x`, but we won't be officially supporting them). You will need to make sure that during your virtual environment setup that the correct version of `python` is used. You can confirm your python version by (1) activating your virtualenv and (2) running `which python`.
+
+**NOTE 2:** As noted in the setup instructions, we strongly recommend you to do development on Google Cloud, as we have limited support for local machine configurations. 
+
+**NOTE 3:** The submission process this year has **2 steps**, requiring you to 1. run a submission script and 2. download/upload an auto-generated pdf (details below.) We suggest **_making a test submission early on_** to make sure you are able to successfully submit your assignment on time (a maximum of 10 successful submissions can be made.)
+
+### Q1: Fully-connected Neural Network (20 points)
+The IPython 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 the IPython notebook `BatchNormalization.ipynb` you will implement batch
+normalization, and use it to train deep fully-connected networks.
+
+### Q3: Dropout (10 points)
+The IPython 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.
+
+**NOTE 1**: The PyTorch notebook requires PyTorch version 1.0, which comes pre-installed on the Google cloud instances.
+
+**NOTE 2**: The TensorFlow notebook requires Tensorflow version 2.0. If you want to work on the Tensorflow notebook with your VM from assignment1, please follow the instructions on [Piazza](https://piazza.com/class/js3o5prh5w378a?cid=384) to install TensorFlow. 
+ New virtual machines that are set up following the [instructions](/setup-instructions) will come with the correct version of Tensorflow.
+
+
+### Submitting your work
+There are **_two_** steps to submitting your assignment:
+
+**1.** Run the provided `collectSubmission.sh` script in the `assignment2` directory.
+
+You will be prompted for your SunetID (e.g. `jdoe`) and will need to provide your Stanford password. This script will generate a zip file of your code, submit your source code to Stanford AFS, and generate a pdf `a2.pdf` in a `cs231n-2019-assignment2/` folder in your AFS home directory. 
+
+If your submission for this step was successful, you should see a display message 
+
+`### Code submitted at [TIME], [N] submission attempts remaining. ###`
+
+**2.** Download the generated `a2.pdf` from AFS, then submit the pdf to [Gradescope](https://gradescope.com/courses/17367).