<br/>CS229 Lecture notes<br/>Andrew Ng<br/>Mixtures of Gaussians and the EM algorithm<br/>In this set of notes, we discuss the EM (Expectation-Maximization) for de..
右侧第三型:variance 具体推导过程见P120 ofhttps://cs229.stanford.edu/main_notes.pdf *注意,这是回归问题的数学式子,分类问题相对not clear,暂无统一数学式子 3、总结bias、variance、model complexity、underfit、overfit、generalization error、training error之间的关系: Find best bias-variance trade-off accordin...
dot(self.w) return y_pred if __name__ == "__main__": lr_gd = LR_GD() lr_gd.fit(x,y) print("估计的参数值为:%s" %(lr_gd.w)) x_test = np.array([2,4,5]).reshape(1,-1) print("预测值为:%s" %(lr_gd.predict(x_test))) ...
Note for cs229note1iSea @ Jan. 4th, 2015Content1. 线性回归(Linear Regression)2. 对数回归(Logistic Regression)3. 一般线性模型(Generalized Linear Model)1 线性回归(Linear Regression)1.1 问题为了预测房价模型,抽出的feature有房间大小和卧室数目两个,如果假定目标模型为线性的,那么学习的过程就是线性回...
代码样本如http://cs229.stanford.edu/notes/backprop.py所示。 (译者注:为了方便我直接把上面链接中的代码贴到下面了。) #http://cs229.stanford.edu/notes/backprop.pyimportnumpyasnpfromcopyimportcopy# Example backpropagation code for binary classification with 2-layer# neural network (single hidden ...
(This was denoted by the Greek alphabet rho,10% which looks like a lower-case"p",11%inthe lecture notes).12lambda =0.0001; %weight decay parameter13beta =3; %weight of sparsity penalty term1415%%===16%% STEP1: Implement sampleIMAGES17%18%After implementing sampleIMAGES, the display_netwo...
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N (?, Σ). Here, recall from the section notes on linear algebra that Sn ++ refers to the space of symmetric positive de?nite n × n matrices.5 Generally speaking, Gaussian random variables are extremely useful in machine learning and statistics for two main reasons. First, they are ...
先验概率:基于经验或调查得到的某事件发生概率P(xi)。比如所有人群中糖尿病发病率 后验概率:由果及某一个因的概率P(xi|y),这就是一个条件概率。比如高血压人群中患有糖尿病的概率 一般来说,先验概率易得,后验概率难得,因此,常用贝叶斯公式由先验概率求后验概率 ...