• Certain features may require a compiler to be installed; (Visual Studio C++ compiler, GCC, clang)
• Code base is stored in separate assignment directories; with ipython notebooks used for running and displaying results;
• Within each assignment directory there is a series of small python modules (that handle feature implementation see assignment1/cs231n);
• Within each assignment directory there is a dataset directory, with a series of scripts to download images (see assignment1/cs231n/datasets)

From Assignment 1 Webpage:

In this assignment you will practice putting together a simple image classification pipeline, based on the k-Nearest Neighbor or the SVM/Softmax classifier. The work flow for the assignment are detailed in README.md. The goals of this assignment are as follows:

• understand the basic Image Classification pipeline and the data-driven approach (train/predict stages)
• understand the train/val/test splits and the use of validation data for hyperparameter tuning.
• develop proficiency in writing efficient vectorized code with numpy
• implement and apply a k-Nearest Neighbor (kNN) classifier
• implement and apply a Multiclass Support Vector Machine (SVM) classifier
• implement and apply a Softmax classifier
• implement and apply a Two layer neural network classifier
• understand the differences and tradeoffs between these classifiers
• get a basic understanding of performance improvements from using higher-level representations than raw pixels (e.g. color histograms, Histogram of Gradient (HOG) features)

From Assignment 2 Webpage:

In this assignment you will practice writing backpropagation code, and training Neural Networks and Convolutional Neural Networks. The work flow for the assignment are detailed in README.md. 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 the core parameter update loop of mini-batch gradient descent
• implement various update rules used to optimize Neural Networks
• implement batch normalization for training deep networks
• implement dropout to regularize networks
• effectively cross-validate and find the best hyperparameters for Neural Network architecture
• understand the architecture of Convolutional Neural Networks and train gain experience with training these models on data

From Assignment 3 Webpage:

In this assignment you will implement recurrent networks, and apply them to image captioning on Microsoft COCO. We will also introduce the TinyImageNet dataset, and use a pretrained model on this dataset to explore different applications of image gradients. The work flow for the assignment are detailed in README.md. The goals of this assignment are as follows:

• Understand the architecture of recurrent neural networks (RNNs) and how they operate on sequences by sharing weights over time
• Understand the difference between vanilla RNNs and Long-Short Term Memory (LSTM) RNNs
• Understand how to sample from an RNN at test-time
• Understand how to combine convolutional neural nets and recurrent nets to implement an image captioning system
• Understand how a trained convolutional network can be used to compute gradients with respect to the input image
• Implement and different applications of image gradients, including saliency maps, fooling images, class visualizations, feature inversion, and DeepDream.

You can work on the assignment in one of two ways: locally on your own machine, or on a virtual machine through Terminal.com. I will only detail how to run in locally.

Get the initial code as a zip file here.

[Use Anaconda] The preferred approach for installing all the assignment dependencies is to use Anaconda, which is a Python distribution that includes many of the most popular Python packages for science, math, engineering and data analysis. Once you install it you can skip all mentions of requirements and you're ready to go directly to working on the assignment.

[Virtual Environment (through pip)] If you choose not to use a virtual environment, it is up to you to make sure that all dependencies for the code are installed globally on your machine. To set up a virtual environment, run the following (assuming you already have pip installed):

cd assignment1
virtualenv .env                  # Create a virtual environment
source .env/bin/activate         # Activate the virtual environment
pip install -r requirements.txt  # Install dependencies
# Work on the assignment for a while ...
deactivate                       # Exit the virtual environment

Alternatively you can use virtualenvwrapper to manage your virtual environments. Note there are few other points:

1. python packages can be installed through package managers. Sometimes one should to use the switch --system-site-packages during virtualenv creation.
2. do not use sudo when calling pip.
3. conda env can be used to install/upgrade packages in a similar to virtual environments (documentation). USE IT

Once you have the starter code, you will need to download the CIFAR-10 dataset. Run the following from the assignment1 directory:

cd cs231n/datasets
./get_datasets.sh

After you have the CIFAR-10 data, you should start the IPython notebook server from the assignment1 directory. If you are unfamiliar with IPython, you should read our IPython tutorial.

NOTE: If you are working in a virtual environment on OSX, you may encounter errors with matplotlib due to the issues described here. You can work around this issue by starting the IPython server using the start_ipython_osx.sh script from the assignment1 directory; the script assumes that your virtual environment is named .env.

The assignments from the prior quarter (Winter 2015). There is a large amount of overlap between the three assignments for Winter 2015-2016. Details of assignment 1, assignment 2, and assignment 3 README.

Segmentation? Object Detection? Classification?