From 92385ba72b0965047b4ae06e68f0e10109b7c11c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=99=8C=E6=BA=AA=5F?= Date: Tue, 3 Nov 2020 22:22:23 +0800 Subject: [PATCH] =?UTF-8?q?docs:=20=E5=A2=9E=E5=8A=A0=E5=8D=9A=E5=AE=A2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../README.md" | 4 + .../README.md" | 2 - .../README.md" | 77 ++++++++++++++++++ .../images/image-20201103221139562.png" | Bin 0 -> 12793 bytes .../images/image-20201103221317190.png" | Bin 0 -> 13373 bytes .../images/image-20201103221452704.png" | Bin 0 -> 32588 bytes .../images/image-20201103221719159.png" | Bin 0 -> 150059 bytes .../images/image-20201103221846982.png" | Bin 0 -> 23662 bytes .../images/image-20201103221918775.png" | Bin 0 -> 33252 bytes .../images/image-20201103221936166.png" | Bin 0 -> 60335 bytes .../images/image-20201103221956484.png" | Bin 0 -> 29030 bytes .../images/image-20201103222116830.png" | Bin 0 -> 118483 bytes 12 files changed, 81 insertions(+), 2 deletions(-) create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/README.md" create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221139562.png" create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221317190.png" create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221452704.png" create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221719159.png" create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221846982.png" create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221918775.png" create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221936166.png" create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221956484.png" create mode 100644 "\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103222116830.png" diff --git "a/K8S/1_Kubernetes\347\256\200\344\273\213/README.md" "b/K8S/1_Kubernetes\347\256\200\344\273\213/README.md" index 39c1d55c..154d92ce 100644 --- "a/K8S/1_Kubernetes\347\256\200\344\273\213/README.md" +++ "b/K8S/1_Kubernetes\347\256\200\344\273\213/README.md" @@ -1,5 +1,9 @@ # Kubernetes简介 +## 来源 + +bilibili尚硅谷K8S视频:https://www.bilibili.com/video/BV1GT4y1A756 + ## 介绍 K8S主要讲的就是Kubernetes,首先Kubernetes首字母为K,末尾为s,中间一共有8个字母,所以简称K8s diff --git "a/Linux/VmWare\347\273\231CentOS\346\211\251\345\256\271/README.md" "b/Linux/VmWare\347\273\231CentOS\346\211\251\345\256\271/README.md" index 369280d8..8480597b 100644 --- "a/Linux/VmWare\347\273\231CentOS\346\211\251\345\256\271/README.md" +++ "b/Linux/VmWare\347\273\231CentOS\346\211\251\345\256\271/README.md" @@ -4,8 +4,6 @@ 今天因为自己的虚拟机满了,无法继续创建Docker镜像,就想着给虚拟机扩容,从原来的20G提升到40G - - ## 扩容步骤 首先我们需要关闭我们正在运行的虚拟机,然后找到设置页面 diff --git "a/\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/README.md" "b/\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/README.md" new file mode 100644 index 00000000..c86d3158 --- /dev/null +++ "b/\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/README.md" @@ -0,0 +1,77 @@ +# Embedding层的作用 + +## 前言 + +在深度学习实验中经常会遇到Embedding层,网络上关于Embedding层介绍非常含糊。Embedding是数学领域的专有名词:指某个对象X被嵌入到另一个对象Y中。Word Embedding 是NLP中一组语言模型和特征学习技术的总称。主要作用是:嵌入层将正整数转换为具有固定大小的词向量 + +## One-Hot编码 + +大家都知道,模型是不会接受原始文本作为输入的,它只能处理数值型数据,因此文本向量化是让模型处理文本的前提。文本向量化是指将文本转换为数值张量的过程。文本向量化的方法有很多种。而one-hot编码是其中的一种。 + +定义:使用 N位状态寄存器对N个状态进行编码,每个状态都是一个独立的寄存器位,并且在任意时候只有一位有效。One-Hot编码是分类变量作为二进制向量的表示。首先要求分类值映射到整数,然后每个整数值都被表示为二进制向量。 + +优势:扩充特征的作用 + +### 例子 + +给定下面的数据,我们进行one-hot编码处理 + +![image-20201103221139562](images/image-20201103221139562.png) + +我们对上面的值进行One-Hot编码后,就会变成下面的内容 + +![image-20201103221317190](images/image-20201103221317190.png) + +### One-Hot编码在文本中的应用 + +one-hot编码在文本中的应用是,它将每个词与一个唯一的整数索引关联,然后将这个整数索引i转换成长度为N(N是词典大小)的二进制向量,这个向量的特点是只有第i个元素是1,其余元素为0。 + +对于这样一句话:我从哪里来,要到何处去,经过 One-Hot编码后 + +![image-20201103221452704](images/image-20201103221452704.png) + +### One-Hot优点 + +- 解决了分类器不好处理离散数据的问题 +- 能够起到扩充特征的作用 + +### One-Hot 缺点 + +- 基于词袋模型,不考虑词与词之间的顺序 +- 得到的特征是离散稀疏的 + +这是它最明显的缺点。过于稀疏时,过度占用资源。其实我们这篇文章,虽然100W字,但是其实我们整合起来,有99W字是重复的,只有1W字是完全不重复的。那我们用100W X 10W的岂不是白白浪费了99W X 10W的矩阵存储空间。这个时候,Embedding层就起作用了 + +## Embedding层 + +为了解决构建语言模型的时候,出现维数灾难,Embedding层的作用就体现出来了,Embedding层的最主要的一个功能就是降维 + +![image-20201103221719159](images/image-20201103221719159.png) + +假设我们有一个 100W * 10W的矩阵,然后乘上10W*20的矩阵,就能够降到100W* * 20了 + +我们将词汇表里的词用 Royalty, Masculinity, Femininity和Age 4个维度来表示 + +![image-20201103221846982](images/image-20201103221846982.png) + +我们将king这个词从一个可能非常稀疏的向量坐在的空间,映射到现在这个四维向量所在的空间这个过程就被称为 word embedding(词嵌入),也就是说我们将一个高维词向量嵌入到一个低维空间中。 + +![image-20201103221918775](images/image-20201103221918775.png) + +通过一系列的降维操作后,我们就很容易分析词与词之间的关系了,假设我们现在降到2维,我能就能发现下面的这样一个有趣的现象 + +![image-20201103221936166](images/image-20201103221936166.png) + +对于一个词,如果我们想要去想它的属性是一件非常困难的事情,所以这个时候,我们只需要交给神经网络,定义好 它的维度,然后通过神经网络去学习它的每一个属性的大小,我们并不需要关心这些属性到底代表着什么,我们只需要知道词向量的夹角越小,表示他们的语言就更加接近。 + +![image-20201103221956484](images/image-20201103221956484.png) + +通过上面的图,我们知道,cat (猫)和 kitty(猫咪) 是非常接近的,这就是Embedding层作用。 + +## 中文词向量 + +一般我们进行模型训练的时候,都是使用别人已经做好的词向量 + +项目地址:https://github.com/Embedding/Chinese-Word-Vectors + +![image-20201103222116830](images/image-20201103222116830.png) \ No newline at end of file diff --git "a/\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221139562.png" "b/\346\234\272\345\231\250\345\255\246\344\271\240/Embedding\345\261\202\347\232\204\344\275\234\347\224\250/images/image-20201103221139562.png" new file mode 100644 index 0000000000000000000000000000000000000000..303fbccda0471e3db15cdd551f77f42b3296335b GIT binary patch literal 12793 zcmcJV1yodTzwb98Ch|9tRsoeX=*A#Mx*J3!#{p?+OhhS30qJHKkQ{n200D;{kbwb| zW(bL)<31bT_nv$1TKBGV?pl|%bl3wt`-xwCzn?w9n(9jQ$C!^n5JZnqzNZaAG|J$6 z!Vx;~KWe>m5P~j3h2t0 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