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nathanpro.com

Nathan Jiang

喜欢做前端,正在走在机器学习的路上。对一切新鲜事物和未知充满兴趣。电子产品发烧友。死命寻找活着的意义。

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Nathan Prosnier

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am●●●●@cox.net

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PAGE TITLE
Nathan Jiang | nathanpro.com Reviews
<META>
DESCRIPTION
喜欢做前端,正在走在机器学习的路上。对一切新鲜事物和未知充满兴趣。电子产品发烧友。死命寻找活着的意义。
<META>
KEYWORDS
1 archives
2 tags
3 nathan jiang
4 书摘 先成为自己的英雄
5 读书笔记
6 一、声色光影由此开始 自述
7 这段话是几乎看完 越后面越没有看的兴趣 整本书回来重看第一章写的
8 刚开始时,会觉得大鹏好强,年轻人却有如此成就,与人和善不自负,
9 看着看着,噢
10 原来他是本山先生的徒弟啊,难怪如此平步青云,
CONTENT
Page content here
KEYWORDS ON
PAGE
archives,tags,nathan jiang,书摘 先成为自己的英雄,读书笔记,一、声色光影由此开始 自述,这段话是几乎看完 越后面越没有看的兴趣 整本书回来重看第一章写的,刚开始时,会觉得大鹏好强,年轻人却有如此成就,与人和善不自负,,看着看着,噢,原来他是本山先生的徒弟啊,难怪如此平步青云,,并没有黑任何人,所有成功的人都是值得敬畏的,generalized linear models,stanford machine learning,algorithm,machine learning
CONTENT-TYPE
utf-8
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Nathan Jiang | nathanpro.com Reviews

https://nathanpro.com

喜欢做前端,正在走在机器学习的路上。对一切新鲜事物和未知充满兴趣。电子产品发烧友。死命寻找活着的意义。

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Mon, Aug 3 2015, 12:10:58 pm. Sun, Jun 21 2015, 3:43:36 pm. 在回归的例子中,服从正态分布 y x;θ N (μ, σ. 在分类的例子中,服从伯努利分布,y x;θ Bernoulli(φ)。 在这节中,我们将说明这两个例子均属于一个模型家族,称为广义线性模型 Generalized Linear Models ,我们将展示GLMS中的其他模型能够均来源于回归、分类问题,及推导过程。 1 指数家族 The exponential family. P(y; eta) = b(y) exp( eta TT(y) - a( eta) ) $. 这里,η表示分布律的自然参数,T(y)表示充分统计量,通常T(y) = y, e. P(y; phi) and = phi y (1- phi) {1-y}. Exp (log( phi y(1- phi) {1-y}) ). Exp ( ylog phi (1-y)log(1- phi) ). Exp ( ylog frac{ phi}{1- phi} log(1- phi) ). B(y) and = 1.

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