CS231n: Convolutional Neural Networks for Visual Recognition - Assignment Solutions

This repository contains my solutions to the assignments of the CS231n course offered by Stanford University which I followed along online(Spring 2019). All the solutions here are ungraded unfortunately because I am not enrolled as an official student

Find course notes and assignments here and be sure to check out Spring 2017!

Assignments using Tensorflow are completed, those using Pytorch will be implemented in the future.

Assignment 1:

• Q1: k-Nearest Neighbor classifier. (Done)
• Q2: Training a Support Vector Machine. (Done)
• Q3: Implement a Softmax classifier. (Done)
• Q4: Two-Layer Neural Network. (Done)
• Q5: Higher Level Representations: Image Features. (Done)

Assignment 2:

• Q1: Fully-connected Neural Network. (Done)
• Q2: Batch Normalization. (Done)
• Q3: Dropout. (Done)
• Q4: Convolutional Networks. (Done)
• Q5: PyTorch / TensorFlow on CIFAR-10. (Done)

Assignment 3:

• Q1: Image Captioning with Vanilla RNNs. (Done)
• Q2: Image Captioning with LSTMs. (Done)
• Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images. (Done)
• Q4: Style Transfer. (Done)
• Q5: Generative Adversarial Networks. (Done)