CS231n: Convolutional Neural Networks for Visual Recognition (Spring 2018)

My solution to assignments in Stanford CS231n: Convolutional Neural Networks for Visual Recognition (Spring 2018).

I would like to express my sincere gratitude and appreciation to all instructors and teaching assistants in CS231n for creating this wonderful course and making it freely available. Thanks for the fantastic video lectures given by Justin Johnson, Serena Yeung and Fei-Fei Li (Spring 2017)!

Assignment 1: Image Classification, kNN, SVM, Softmax, Neural Network

• 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: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets

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

Assignment 3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks

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