Generative Lecture 4: Autoregressive Models (AR)
The note introduces autoregressive models, covering their training, evaluation, decoding strategies, RNN and Transformer implementations, and computational complexity.
そして扉がひらくよ——

学习笔记与生活文章,按发布时间排列。一页笔记,一点灵感,一些值得珍藏的日常。
The note introduces autoregressive models, covering their training, evaluation, decoding strategies, RNN and Transformer implementations, and computational complexity.
This lecture covers geometric transformations, feature matching with RANSAC, and image blending using Laplacian pyramids and Poisson editing to create seamless panoramas.
月亮
以 CS336 Assignment 1 为基础,从零实现 byte-level BPE Tokenizer,并通过增量更新、优先队列与多进程预分词优化训练性能
以MLP分类MNIST数据集为例,学习Pytorch用法并加深对模型架构与代码结构的理解