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3 changes: 3 additions & 0 deletions .gitignore
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Expand Up @@ -26,6 +26,9 @@ agreement.jpg
dlbook_cn_public.tex
dlbook_cn_public.bib
font
docs/_site/
docs/images/

main.bib
main.tex

4 changes: 1 addition & 3 deletions Chapter12/applications.tex
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% !Mode:: "TeX:UTF-8"
% Translator:
% Tianfan Fu: 12.1~12. 3
% Shenjian Zhao: 12.4~12.5
% Translator: Tianfan Fu: 12.1~12.3 Shenjian Zhao: 12.4~12.5
\chapter{应用}
\label{chap:applications}

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4 changes: 2 additions & 2 deletions Chapter15/representation_learning.tex
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Expand Up @@ -59,7 +59,7 @@ \chapter{\glsentrytext{representation_learning}}

% -- 518 --

\section{贪心地逐层\glsentrytext{unsupervised_pretraining}}
\section{贪心地逐层\glsentrytext{unsupervised_pretraining} }
\label{sec:greedy_layer_wise_unsupervised_pretraining}
\gls{unsupervised_learning}在\gls{DNN}的复兴历史上起到了关键作用,使研究者首次可以训练不含诸如\gls{convolution}或者\gls{recurrence}这类特殊结构的深度监督网络。
我们将这一过程称为\firstgls{unsupervised_pretraining},或者更精确地,\firstgls{greedy_layer_wise_unsupervised_pretraining}。
Expand All @@ -71,7 +71,7 @@ \section{贪心地逐层\glsentrytext{unsupervised_pretraining}}
\algref{alg:pretraining}所示正式的表述。

\begin{algorithm}
\caption{{\em \gls{greedy_layer_wise_unsupervised_pretraining}的协定}\\
\caption{ {\em \gls{greedy_layer_wise_unsupervised_pretraining}的协定}\\
给定如下:无监督特征学习算法$\CalL$$\CalL$使用训练集样本并返回\gls{encoder}或特征函数$f$
原始输入数据是$\MX$,每行一个样本,并且$f^{(1)}(\MX)$是第一阶段\gls{encoder}关于$\MX$的输出。
在执行\gls{fine_tune}的情况下,我们使用学习者$\CalT$,并使用初始函数$f$,输入样本$\MX$(以及在监督\gls{fine_tune}情况下关联的目标$\MY$),并返回细调好函数。 阶段数为$m$
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2 changes: 1 addition & 1 deletion Chapter2/linear_algebra.tex
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Expand Up @@ -909,7 +909,7 @@ \section{实例:\glsentrytext{PCA}}

此时,使用单一矩阵来重述问题,比将问题写成求和形式更有帮助。
这有助于我们使用更紧凑的符号。
$\MX\in\SetR^{m\times n}$是将描述点的向量堆叠在一起的矩阵,例如$\MX_{i,:}=\Vx^{{(i)}^\top}$
$\MX\in\SetR^{m\times n}$是将描述点的向量堆叠在一起的矩阵,例如$\MX_{i,:}=\Vx^{(i)^\top}$
原问题可以重新表述为:
\begin{equation}
\Vd^* = \underset{\Vd}{\arg\min} \norm{\MX - \MX\Vd\Vd^\top}_F^2
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2 changes: 1 addition & 1 deletion Chapter6/deep_feedforward_networks.tex
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Expand Up @@ -1034,7 +1034,7 @@ \subsection{通用近似性质和深度}
\else
\centerline{\includegraphics[width=\textwidth]{Chapter6/figures/space_folding.jpg}}
\fi
\caption{% This is a screenshot.
\caption{ % This is a screenshot.
关于更深的整流网络具有指数优势的一个直观的几何解释,来自\cite{Montufar-et-al-NIPS2014}。
\emph{(左)}\gls{absolute_value_rectification}单元对其输入中的每对镜像点有相同的输出。
镜像的对称轴由单元的权重和偏置定义的超平面给出。 在该单元顶部计算的函数(绿色决策面)将是横跨该对称轴的更简单模式的一个镜像。
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2 changes: 2 additions & 0 deletions docs/Gemfile
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source 'https://rubygems.org'
gem 'github-pages', group: :jekyll_plugins
200 changes: 200 additions & 0 deletions docs/Gemfile.lock
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GEM
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PLATFORMS
ruby

DEPENDENCIES
github-pages

BUNDLED WITH
1.13.7
1 change: 0 additions & 1 deletion docs/README.md

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93 changes: 92 additions & 1 deletion docs/_config.yml
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theme: jekyll-theme-cayman
title: Deep Learning Book Chinese Translation
description: Deep Learning Book Chinese Translation
disqus_shortname: exacity
reading_time: false
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# Your site's domain goes here (eg: https://mmistakes.github.io, http://yourdomain.com, etc)
# When testing locally leave blank or use http://localhost:4000
baseurl: /deeplearningbook-chinese
url: https://exacity.github.io/deeplearningbook-chinese

# Owner/author information
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avatar:
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# Background image to be tiled on all pages
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# Analytics and webmaster tools stuff goes here
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google_verify:
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# http://en.wikipedia.org/wiki/List_of_tz_database_time_zones
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1 change: 1 addition & 0 deletions docs/_includes/browser-upgrade.html
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23 changes: 23 additions & 0 deletions docs/_includes/disqus_comments.html
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{% if site.disqus_shortname %}
<script type="text/javascript">
/* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
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