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 (Spring 2018).

Find course notes and assignments here and be sure to check out the video lectures for Winter 2016 and 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)