My solutions (and more) for CS231n Assingments in 2022.

Here is the link for them. Use assignment2 and assignment3 for the other urls.

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 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 as opposed to raw pixels, e.g. color histograms, Histogram of Oriented Gradient (HOG) features, etc.

The notebook numpy_review.ipynb is there for you to discover some basic usage of Numpy!

The notebook knn.ipynb will walk you through implementing the kNN classifier.

The notebook svm.ipynb will walk you through implementing the SVM classifier.

The notebook softmax.ipynb will walk you through implementing the Softmax classifier.

The notebook two_layer_net.ipynb will walk you through the implementation of a two-layer neural network classifier.

The notebook features.ipynb will examine the improvements gained by using higher-level representations as opposed to using raw pixel values.

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.
• Explore various applications of image gradients, including saliency maps, fooling images, class visualizations.

The notebook FullyConnectedNets.ipynb will have you implement fully connected networks of arbitrary depth. To optimize these models you will implement several popular update rules.

In notebook BatchNormalization.ipynb you will implement batch normalization, and use it to train deep fully connected networks.

The notebook Dropout.ipynb will help you implement dropout and explore its effects on model generalization.

In the notebook ConvolutionalNetworks.ipynb you will implement several new layers that are commonly used in convolutional networks.

For this part, you will be working with PyTorch, a popular and powerful deep learning framework. Open up PyTorch.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.

There was also a Network-Visualization question, but that is moved to Assignment 3.