字里医间Paper to Practice 斯坦福的免费课程，讲现代 AI 背后的概率，每十名学生配一位志愿老师。
NLP 学习笔记 ·
从随机变量和概率质量函数出发，经过期望与方差，到伯努利、二项和泊松三种分布，再看改变词的概率怎样给 AI 生成的文字加上水印。
From random variables and the probability mass function through expectation and variance to the Bernoulli, binomial and Poisson distributions, and how changing word probabilities lets an AI text carry a watermark.
NLP 学习笔记 ·
从概率符号的读法出发，经过柯尔莫哥洛夫三大公理、补集与“或”规则、条件概率，到独立性：什么时候两个概率可以直接相乘，什么时候加入条件会改变这一点。
From probability symbols and how to read them through Kolmogorov's three axioms, the complement and OR rules and conditional probability to independence: when two probabilities can simply be multiplied, and when conditioning changes that.
NLP 学习笔记 ·
一门讲现代 AI 背后概率的斯坦福免费课：谁在教、每十名学生一位志愿老师的模式、每周的小组课，以及六周的安排。
A free Stanford course on the probability behind modern AI: who teaches it, the one-teacher-per-ten-students model, the weekly section, and the six-week plan.
