CS231N

Deep Learning for Computer Vision

共 18 份讲义。

讲义	日期	来源	资源
CS231N Lecture 1: Introduction	2025年4月1日	Stanford Online	LaTeX
CS231N Lecture 2: Image Classification with Linear Classifiers	2025年4月3日	Stanford CS231N	LaTeX
CS231N Lecture 3: Regularization and Optimization	2025年4月8日	Stanford CS231N	LaTeX
CS231N Lecture 4: Neural Networks and Backpropagation	2025年4月10日	Stanford CS231N	LaTeX
CS231N Lecture 5: Image Classification with CNNs	2025	Stanford Online	LaTeX
CS231N Lecture 6: CNN Architectures	2025	Stanford Online	LaTeX
CS231N Lecture 7: Recurrent Neural Networks	2025	Stanford University	LaTeX
CS231N Lecture 8: Attention and Transformers	2025	Stanford University	LaTeX
CS231N Lecture 9: Object Detection and Image Segmentation	2025	Stanford University	LaTeX
CS231N Lecture 10: Video Understanding	2025年5月1日	Stanford CS231n 10th Anniversary	LaTeX
CS231N Lecture 11: Large Scale Distributed Training	2025年	Stanford Online	LaTeX
CS231N Lecture 12: Self-Supervised Learning	2025年5月8日	Stanford CS231n 10th Anniversary	LaTeX
CS231N Lecture 13: Generative Models 1	2025年	Stanford University	LaTeX
CS231N Lecture 14: Generative Models (Part 2)	2025年5月20日	Stanford Online	LaTeX
CS231N Lecture 15: 3D Vision	2024	Stanford CS231N	LaTeX
CS231N Lecture 16: Multi-Modal Foundation Models	2025-05-27	Stanford CS231N	LaTeX
CS231N Lecture 17: Robot Learning	2025	Stanford CS231N	LaTeX
CS231N Lecture 18: What We See and What We Value	2025	Stanford Online	LaTeX