Blog
On vision and language
CS231n Notes 16: Vision and language
On 3D vision
CS231n Notes 15: 3D vision
On diffusion models
CS231n Notes 14: Diffusion models
On generative models
CS231n Notes 13: Generative models
On self-supervised learning
CS231n Notes 12: Self-supervised learning
On video understanding
CS231n Notes 11: Video understanding
On world models
Understanding space and imagining what happens next.
On object detection, segmentation, and model interpretation
CS231n Notes 10: Object detection, segmentation, and model interpretation
On attention and transformers
CS231n Notes 9: Attention and transformers
On RNNs
CS231n Notes 8: Recurrent Neural Networks
On backpropagation
CS231n Notes 6: Neural Networks and Backpropagation
On CNNs
CS231n Notes 7: Convolutional Neural Networks
On optimization
CS231n Notes 5: Optimization
On regularization
CS231n Notes 4: Understanding regularization
On loss functions
CS231n Notes 3: softmax and cross-entropy loss
On linear classifiers
CS231n Notes 1: A linear classifier
On nearest neighbor
CS231n Notes 1: Nearest neighbor for image classification
3D Gaussian Splatting
From photographs to a scene you can move through.