CS231n - Deep Learning for Computer Vision Stanford - Spring 2023 Assignments Assignment 1: Deep Learning Basics Code Q1: k-Nearest Neighbor classifier Q2: Training a Support Vector Machine Q3: Implement a Softmax classifier Q4: Two-Layer Neural Network Q5: Higher Level Representations: Image Features Additional Notes A1: Calculate Distances A2: Support Vector Machine A3: Softmax A4: Neural Network Assignment 2: Perceiving and Understanding the Visual World Code Q1: Multi-Layer Fully Connected Neural Networks Q2: Batch Normalization Q3: Dropout Q4: Convolutional Neural Networks Q5: PyTorch on CIFAR-10 Additional Notes A1: Neural Network Basics A2: Gray Scale Convolution A3: RGB Scale Convolution A4: Batch RGB Scale Convolution A5: Batch RGB Scale Convolution with Padding and Pooling A6: Convolution Benchmarks A7: Convolution Backward A8: Pooling Layer Details A9: Generate Patches A10: MLP-Mixer A11: MetaFormer Assignment3: Generative and Interactive Visual Intelligence Code Q1: Network Visualization: Saliency Maps, Class Visualization, and Fooling Images Q2: Image Captioning with Vanilla RNNs Q3: Image Captioning with Transformers Q4: Generative Adversarial Networks Q5: Self-Supervised Learning for Image Classification Extra Credit: Image Captioning with LSTMs Additional Notes A1: Positional Encoding A2: Attention A3: SimCLR