Generative Lecture 4: Autoregressive Models (AR)
The note introduces autoregressive models, covering their training, evaluation, decoding strategies, RNN and Transformer implementations, and computational complexity.
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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
以 BPE 预分词代码为例,整理几项常用的Python工程习惯
对于cs61a中课程Lecture 15-17的知识点整理
对于cs61a中课程Lecture 9-14的知识点整理
对于cs61a中课程Lecture 2-7的知识点整理
博客上线噜