RBM Formula Deduction

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Energy based Model the probability distribution (softmax function): \[p(x)=\frac{\exp(-E(x))}{\sum\limits_x{\exp(-E(x))}}\] when there are hidden units, \[P(x)=\sum\limits_h{P(x,h)}=\frac{1}{\sum_x\exp(-E(x))}\sum\limits_h{\exp(-E(x,h))}\] now, we de…

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Sigmoid Function \[ \sigma(z)=\frac{1}{1+e^{(-z)}} \] feature: axial symmetry: \[ \sigma(z)+ \sigma(-z)=1 \] gradient: \[ \frac{\partial\sigma(z)}{\partial z} = \sigma(z)[1-\sigma(z)] \] 由性质1 可知, \[ \frac{\partial\sigma(z)}{\partial z} = \sigma(z) \s…

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Paper Reference: word2vec Parameter Learning Explained 1. One-word context Model In our setting, the vocabulary size is $V$, and the hidden layer size is $N$. The input $x$ is a one-hot representation vector, which means for a given input context wor…

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RBM包含两个层,可见层(visble layer)和隐藏层(hidden layer).神经元之间的连接具有以下特点:层内无连接,层间全连接.RBM可以看做是一个二分图(神经元当做顶点,神经元之间的连接当做边,)所有节点都是随机二值变量节点(只能取0或者1).可见层单元用来描述观察数据的一个方面或一个特征,例如,对于黑白图片而言,一个可见单元可能描述的就是图片某处是否为白色.可见层中单元的个数与观测数据的维数相同,用于输入观测数据,隐层则用于刻画观测数据每个维度之间的依赖关系. n_v,n_h…

2-3. Using Type Deduction

Type Deduction 发生在编译时期可以对一般类型,自定义类型进行类型自推导下面有两个例子: 1． Using auto with a class #include <iostream> #include <typeinfo> using namespace std; class MyClass { }; int main() { auto variable = MyClass(); cout << "Type of variable: "…

salesforce 零基础开发入门学习（十五）salesforce中formula的使用（不含Date/Time）

本文参考官方的formula介绍PDF:https://resources.docs.salesforce.com/200/latest/en-us/sfdc/pdf/salesforce_useful_formula_fields.pdf formula函数A-H:https://help.salesforce.com/apex/HTViewHelpDoc?id=customize_functions_a_h.htm&language=en_US formula函数I-Z:https://he…