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Section05-Neural-Networks-and-Language-Modeling/section05-test.tex

+% !Mode:: "TeX:UTF-8"
+% !TEX encoding = UTF-8 Unicode
+
+\def\CTeXPreproc{Created by ctex v0.2.13, don't edit!}
+\documentclass[cjk,t,compress,12pt]{beamer}
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+\usepackage{eso-pic,graphicx}
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+\usepackage{tikz-qtree}
+\usepackage{hyperref}
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+
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+\end{CJK}
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+\setbeamerfont*{frametitle}{size=\large,series=\bfseries}
+\setbeamertemplate{navigation symbols}{\begin{CJK}{UTF8}{\mycfont} 第五章 神经网络和语言模型 \hspace*{2em} 肖桐\&朱靖波 \end{CJK} \hspace*{2em} \today \hspace*{2em} \insertframenumber{}/\inserttotalframenumber}
+
+\setbeamertemplate{itemize items}[circle] % if you want a circle
+\setbeamertemplate{itemize subitem}[triangle] % if you wnat a triangle
+\setbeamertemplate{itemize subsubitem}[ball] % if you want a ball
+
+\begin{document}
+
+\begin{CJK}{UTF8}{\mycfont}
+
+\title{\Large{神经网络和语言模型}}
+\author{\large{\textbf{肖桐\ \ 朱靖波}}}
+\institute{
+\blue{\url{xiaotong@mail.neu.edu.cn}} \black{} \\
+\blue{\url{zhujingbo@mail.neu.edu.cn}} \black{} \\
+\vspace{1.0em}
+东北大学 自然语言处理实验室 \\
+\blue{\underline{\url{http://www.nlplab.com}}} \black{} \\
+\vspace{0.2cm}
+\hspace{0.1cm} \includegraphics[scale=0.1]{../Figures/logo.pdf}
+}
+\date{}
+
+\maketitle
+
+\setlength{\leftmargini}{1em}
+\setlength{\leftmarginii}{1em}
+
+%%%------------------------------------------------------------------------------------------------------------
+\section{为什么要谈神经网络}
+
+%%%------------------------------------------------------------------------------------------------------------
+\subsection{数学基础：张量计算}
+
+%%%------------------------------------------------------------------------------------------------------------
+%%% 层的概念
+\begin{frame}{``层"的概念}
+\begin{itemize}
+\item 对于一个问题（相同输入），可能会有多个输出，这时可以把\alert{多个相同的神经元并列起来}，构成一\alert{``层"}
+    \begin{itemize}
+    \item 比如，天气预报需要同时预测湿度和温度
+    \end{itemize}
+\end{itemize}
+
+\vspace{-2em}
+
+\begin{center}
+\begin{tikzpicture}
+\begin{scope}
+
+\tikzstyle{neuronnode} = [minimum size=1.5em,circle,draw,ublue,very thick,fill=white,drop shadow={shadow xshift=0.1em,shadow yshift=-0.1em}]
+
+\node [anchor=center,neuronnode] (neuron00) at (0,0) {};
+\node [anchor=center,neuronnode] (neuron01) at ([yshift=-3em]neuron00) {};
+\node [anchor=center,neuronnode] (neuron02) at ([yshift=-3em]neuron01) {};
+
+\node [anchor=east] (x0) at ([xshift=-6em]neuron00.west) {$x_0$};
+\node [anchor=east] (x1) at ([xshift=-6em]neuron01.west) {$x_1$};
+\node [anchor=east] (x2) at ([xshift=-6em]neuron02.west) {$b$};
+
+\node [anchor=west] (y0) at ([xshift=4em]neuron00.east) {$y_0$};
+\node [anchor=west] (y1) at ([xshift=4em]neuron01.east) {$y_1$};
+\node [anchor=west] (y2) at ([xshift=4em]neuron02.east) {$y_2$};
+
+\draw [->] (x0.east) -- (neuron00.180) node [pos=0.3,above] {\tiny{$w_{00}$}};
+\draw [->] (x1.east) -- (neuron00.200) node [pos=0.1,above] {\tiny{$w_{10}$}};
+\draw [->] (x2.east) -- (neuron00.220) node [pos=0.05,above,yshift=0.3em] {\tiny{$b_{0}$}};
+\draw [->] (neuron00.east) -- (y0.west);
+
+\draw [->] (x0.east) -- (neuron01.160) node [pos=0.4,above] {\tiny{$w_{01}$}};
+\draw [->] (x1.east) -- (neuron01.180) node [pos=0.35,above,yshift=-0.2em] {\tiny{$w_{11}$}};
+\draw [->] (x2.east) -- (neuron01.200) node [pos=0.4,below] {\tiny{$b_{1}$}};
+\draw [->] (neuron01.east) -- (y1.west);
+
+\draw [->] (x0.east) -- (neuron02.140) node [pos=0.1,below,yshift=-0.2em] {\tiny{$w_{02}$}};
+\draw [->] (x1.east) -- (neuron02.160) node [pos=0.1,below] {\tiny{$w_{12}$}};
+\draw [->] (x2.east) -- (neuron02.180) node [pos=0.3,below] {\tiny{$b_{2}$}};
+\draw [->] (neuron02.east) -- (y2.west);
+
+\node [anchor=east,align=left] (inputlabel) at ([xshift=-0.1em]x1.west) {输入向量:\\\small{$\textbf{x}=(x_0,x_1)$}};
+\node [anchor=west,align=left] (outputlabel) at ([xshift=0.1em]y1.east) {输出向量:\\\small{$\textbf{y}=(y_0,y_1,y_2)$}};
+
+\begin{pgfonlayer}{background}
+\node [rectangle,inner sep=0.4em,fill=red!20] [fit = (neuron00) (neuron01) (neuron02)] (layer) {};
+\node [anchor=south] (layerlabel) at ([yshift=0.2em]layer.north) {一层神经元};
+
+\node [rectangle,inner sep=0.1em,fill=ugreen!20] [fit = (x0) (x1)] (inputshadow) {};
+\node [rectangle,inner sep=0.1em,fill=blue!20] [fit = (y0) (y1) (y2)] (outputshadow) {};
+\end{pgfonlayer}
+
+\node [anchor=north west] (wlabel) at ([yshift=-1em,xshift=-7em]x2.south) {参数(矩阵):$\textbf{w} = \Big( \begin{array}{lll} w_{01} & w_{01} & w_{02} \\ w_{11} & w_{11} & w_{12} \end{array} \Big)$};
+\node [anchor=west] (blabel) at (wlabel.east) {参数(向量):$\textbf{b} = (b_0, b_1, b_2)$};
+
+\end{scope}
+\end{tikzpicture}
+\end{center}
+
+\end{frame}
+
+%%%------------------------------------------------------------------------------------------------------------
+\subsection{多层神经网络}
+
+%%%------------------------------------------------------------------------------------------------------------
+\subsection{参数学习 - 反向传播}
+
+\end{CJK}
+\end{document}