Need help with CS231n?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

224 Stars 132 Forks 45 Commits 18 Opened issues


My assignment solutions for CS231n - Convolutional Neural Networks for Visual Recognition

Services available


Need anything else?

Contributors list

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)

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.