CS231n Convolutional Neural Networks for Visual Recognition

This repository contains my solution for the cs231n of Stanford University.
If you have any questions, feel free to contact me via e-mail(ding@ivanpp.me).

Assignment1

• Q1: k-Nearest Neighbor classifier (20 points)
• Q2: Training a Support Vector Machine (25 points)
• Q3: Implement a Softmax classifier (20 points)
• Q4: Two-Layer Neural Network (25 points)
• Bonus: Implement some extra techniques on Two-Layer Neural Network (5 points)
  see: ./Assignment1/cs231n/classifiers/neural_net.py and ./Assignment1/two_layer_net.ipynb
  I implement the dropconnect in neural_net.py and get 57.1% test accuracy in two_layer_net.ipynb
• Q5: Higher Level Representations: Image Features (10 points)
• Bonus: Design your own features!
• Q6: Cool Bonus: Do something extra! (+10 points)

Assignment2

• Q1: Fully-connected Neural Network (25 points)
• Q2: Batch Normalization (25 points)
• Bonus: Batch Normalization: alternative backward (3 points)
  see: batchnorm_backward_alt() function in ./Assignment2/cs231n/layers.py
• Q3: Dropout (10 points)
• Q4: Convolutional Networks (30 points)
• Q5: PyTorch / Tensorflow on CIFAR-10 (10 points)
• Bonus: Do something extra! (up to 10 points)

Assignment3

• Q1: Image Captioning with Vanilla RNNs (25 points)
• Q2: Image Captioning with LSTMs (30 points)
• Bonus: Train a good captioning model!
• Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images (15 points)
• Q4: Style Transfer (15 points)
• Q5: Generative Adversarial Networks (15 points)
• Bonus: WGAN-GP or something cool.