CS231N

Course homework for Stanford cs231n course, Deep Learning for Computer Vision.

Contents

• Assignment 1:
  • knn.ipynb: K nearest neighbors algorithm
  • svm.ipynb: Support vector machine with hinge loss
  • softmax.ipynb: Softmax classifier with cross-entropy loss
  • two_layer_net.ipynb: Two-layer neural network
• Assignment 2:
  • FullyConnectedNets.ipynb: Fully connected neural network
  • BatchNormalization.ipynb: Batch normalization layer
  • Dropout.ipynb: Dropout layer
  • ConvolutionalNetworks.ipynb: Convolutional neural network
  • PyTorch.ipynb: PyTorch framework
• Assignment 3:
  • RNN_Captioning.ipynb: Image captioning with vanilla RNN
  • Transformer_Captioning.ipynb: Image captioning with transformer
  • Generative_Adversarial_Networks.ipynb: Generative adversarial networks
  • Self_Supervised_Learning.ipynb: Self-supervised learning with contrastive loss
  • LSTM_Captioning.ipynb: Image captioning with LSTM (Optional)