CS231N: Deep Learning for Computer Vision

My solutions to Stanford's 2018 offering of CS231N, completed in TensorFlow.

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: TensorFlow on CIFAR-10. (Done)
• Q5: PyTorch 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)