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ISBN：9787115452573

商品编码：13791776441

3本 NLTK基础教程 用NLTK和Python库构建机器学习应用+NLP汉语自然语言处理原理+深度学习:原理与应用实践 9787115452573 9787121307652 9787121304132

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• 书 号： 978-7-115-45257-3
• 页 数： 172
• 印刷方式： 黑白印刷
• 开 本： 16开
• 出版状态： 正在印刷
• 原书名： 
• 原书号： 978-1784396909

NLTK基础教程——用NLTK和Python库构建机器学习应用[预售]

• 作者： 
• 译者：  责编： 
• 分类： 

【预计上市时间：05月23日】
NLTK 库是当前自然语言处理（NLP）领域·为流行、使用·为广泛的库之一, 同时Python语言也已逐渐成为主流的编程语言之一。
本书主要介绍如何通过NLTK库与一些Python库的结合从而实现复杂的NLP任务和机器学习应用。全书共分为10章。第1章对NLP进行了简单介绍。第2章、第3章和第4章主要介绍一些通用的预处理技术、专属于NLP领域的预处理技术以及命名实体识别技术等。第5章之后的内容侧重于介绍如何构建一些NLP应用，涉及文本分类、数据科学和数据处理、社交媒体挖掘和大规模文本挖掘等方面。
本书适合 NLP 和机器学习领域的爱好者、对文本处理感兴趣的读者、想要快速学习NLTK的资深Python程序员以及机器学习领域的研究人员阅读。

作 译 者：

出版时间：2017-01 千 字 数：816 版    次：01-01 页    数：544 印刷时间： 开    本：16开 印    次：01-01 装    帧： I S B N ：9787121307652   重    印：新书 换    版：

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本书是一本研究汉语自然语言处理方面的基础性、综合性书籍，涉及NLP的语言理论、算法和工程实践的方方面面，内容繁杂。 本书包括NLP的语言理论部分、算法部分、案例部分，涉及汉语的发展历史、传统的句法理论、认知语言学理论。需要指出的是，本书是迄今为止，本系统介绍认知语言学和算法设计相结合的中文NLP书籍，并从认知语言学的视角重新认识和分析了NLP的句法和语义相结合的数据结构。这也是本书的创新之处。 本书适用于所有想学习NLP的技术人员，包括各大人工智能实验室、软件学院等专业机构。

目  录

第1章  中文语言的机器处理    1

1.1  历史回顾    2

1.1.1  从科幻到现实    2

1.1.2  早期的探索    3

1.1.3  规则派还是统计派    3

1.1.4  从机器学习到认知

计算    5

1.2  现代自然语言系统简介    6

1.2.1  NLP流程与开源框架    6

1.2.2  哈工大NLP平台及其

演示环境    9

1.2.3  Stanford NLP团队及其

演示环境    11

1.2.4  NLTK开发环境    13

1.3  整合中文分词模块    16

1.3.1  安装Ltp Python组件    17

1.3.2  使用Ltp 3.3进行中文

分词    18

1.3.3  使用结巴分词模块    20

1.4  整合词性标注模块    22

1.4.1  Ltp 3.3词性标注    23

1.4.2  安装StanfordNLP并

编写Python接口类    24

1.4.3  执行Stanford词性

标注    28

1.5  整合命名实体识别模块    29

1.5.1  Ltp 3.3命名实体识别    29

1.5.2  Stanford命名实体

识别    30

1.6  整合句法解析模块    32

1.6.1  Ltp 3.3句法依存树    33

1.6.2  Stanford Parser类    35

1.6.3  Stanford短语结构树    36

1.6.4  Stanford依存句法树    37

1.7  整合语义角色标注模块    38

1.8  结语    40

第2章  汉语语言学研究回顾    42

2.1  文字符号的起源    42

2.1.1  从记事谈起    43

2.1.2  古文字的形成    47

2.2  六书及其他    48

2.2.1  象形    48

2.2.2  指事    50

2.2.3  会意    51

2.2.4  形声    53

 

2.2.5  转注    54

2.2.6  假借    55

2.3  字形的流变    56

2.3.1  笔与墨的形成与变革    56

2.3.2  隶变的方式    58

2.3.3  汉字的符号化与结构    61

2.4  汉语的发展    67

2.4.1  完整语义的基本

形式——句子    68

2.4.2  语言的初始形态与

文言文    71

2.4.3  白话文与复音词    73

2.4.4  白话文与句法研究    78

2.5  三个平面中的语义研究    80

2.5.1  词汇与本体论    81

2.5.2  格语法及其框架    84

2.6  结语    86

第3章  词汇与分词技术    88

3.1  中文分词    89

3.1.1  什么是词与分词规范    90

3.1.2  两种分词标准    93

3.1.3  歧义、机械分词、语言

模型    94

3.1.4  词汇的构成与未登录

词    97

3.2  系统总体流程与词典结构    98

3.2.1  概述    98

3.2.2  中文分词流程    99

3.2.3  分词词典结构    103

3.2.4  命名实体的词典

结构    105

3.2.5  词典的存储结构    108

3.3  算法部分源码解析    111

3.3.1  系统配置    112

3.3.2  Main方法与例句    113

3.3.3  句子切分    113

3.3.4  分词流程    117

3.3.5  一元词网    118

3.3.6  二元词图    125

3.3.7  NShort算法原理    130

3.3.8  后处理规则集    136

3.3.9  命名实体识别    137

3.3.10  细分阶段与·短

路径    140

3.4  结语    142

第4章  NLP中的概率图模型    143

4.1  概率论回顾    143

4.1.1  多元概率论的几个

基本概念    144

4.1.2  贝叶斯与朴素贝叶斯

算法    146

4.1.3  文本分类    148

4.1.4  文本分类的实现    151

4.2  信息熵    154

4.2.1  信息量与信息熵    154

4.2.2  互信息、联合熵、

条件熵    156

4.2.3  交叉熵和KL散度    158

4.2.4  信息熵的NLP的

意义    159

4.3  NLP与概率图模型    160

4.3.1  概率图模型的几个

基本问题    161

4.3.2  产生式模型和判别式

模型    162

4.3.3  统计语言模型与NLP

算法设计    164

4.3.4  极大似然估计    167

4.4  隐马尔科夫模型简介    169

4.4.1  马尔科夫链    169

4.4.2  隐马尔科夫模型    170

4.4.3  HMMs的一个实例    171

4.4.4  Viterbi算法的实现    176

4.5  ·大熵模型    179

4.5.1  从词性标注谈起    179

4.5.2  特征和约束    181

4.5.3  ·大熵原理    183

4.5.4  公式推导    185

4.5.5  对偶问题的极大似然

估计    186

4.5.6  GIS实现    188

4.6  条件随机场模型    193

4.6.1  随机场    193

4.6.2  无向图的团（Clique）

与因子分解    194

4.6.3  线性链条件随机场    195

4.6.4  CRF的概率计算    198

4.6.5  CRF的参数学习    199

4.6.6  CRF预测标签    200

4.7  结语    201

第5章  词性、语块与命名实体

识别    202

5.1  汉语词性标注    203

5.1.1  汉语的词性    203

5.1.2  宾州树库的词性标注

规范    205

5.1.3  stanfordNLP标注

词性    210

5.1.4  训练模型文件    213

5.2  语义组块标注    219

5.2.1  语义组块的种类    220

5.2.2  细说NP    221

5.2.3  细说VP    223

5.2.4  其他语义块    227

5.2.5  语义块的抽取    229

5.2.6  CRF的使用    232

5.3  命名实体识别    240

5.3.1  命名实体    241

5.3.2  分词架构与专名

.............

深度学习：原理与应用实践

丛书名 ： 著    者： 作 译 者： 出版时间：2016-12 千 字 数：259 版    次：01-01 页    数：232 印刷时间： 开    本：16开 印    次：01-01 装    帧： I S B N ：9787121304132   重    印：新书 换    版： 所属分类： >>  >>  广告语：    纸质书定价：￥48

 

 

本书全面、系统地介绍深度学习相关的技术，包括人工神经网络，卷积神经网络，深度学习平台及源代码分析，深度学习入门与进阶，深度学习高级实践，所有章节均附有源程序，所有实验读者均可重现，具有高度的可操作性和实用性。通过学习本书，研究人员、深度学习爱好者，能够在3 个月内，系统掌握深度学习相关的理论和技术。

目 录

深度学习基础篇

第1 章 绪论 ·································································································· 2

1.1 引言 ······································································································· 2

1.1.1 Google 的深度学习成果 ···························································· 2

1.1.2 Microsoft 的深度学习成果························································· 3

1.1.3 国内公司的深度学习成果 ························································· 3

1.2 深度学习技术的发展历程 ···································································· 4

1.3 深度学习的应用领域 ············································································ 6

1.3.1 图像识别领域 ············································································· 6

1.3.2 语音识别领域 ············································································· 6

1.3.3 自然语言理解领域 ····································································· 7

1.4 如何开展深度学习的研究和应用开发 ················································· 7

本章参考文献 ······························································································ 11

第2 章 国内外深度学习技术研发现状及其产业化趋势 ······························· 13

2.1 Google 在深度学习领域的研发现状 ·················································· 13

2.1.1 深度学习在Google 的应用 ······················································ 13

2.1.2 Google 的TensorFlow 深度学习平台 ······································ 14

2.1.3 Google 的深度学习芯片TPU ·················································· 15

2.2 Facebook 在深度学习领域的研发现状 ·············································· 15

2.2.1 Torchnet ···················································································· 15

2.2.2 DeepText ··················································································· 16

2.3 百度在深度学习领域的研发现状 ······················································· 17

2.3.1 光学字符识别 ··········································································· 17

2.3.2 商品图像搜索 ··········································································· 17

2.3.3 在线广告 ·················································································· 18

2.3.4 以图搜图 ·················································································· 18

2.3.5 语音识别 ·················································································· 18

2.3.6 百度开源深度学习平台MXNet 及其改进的深度语音识别系统Warp-CTC ····· 19

2.4 阿里巴巴在深度学习领域的研发现状 ··············································· 19

2.4.1 拍立淘 ······················································································ 19

2.4.2 阿里小蜜——智能客服Messenger ········································· 20

2.5 京东在深度学习领域的研发现状 ······················································· 20

2.6 腾讯在深度学习领域的研发现状 ······················································· 21

2.7 科创型公司（基于深度学习的人脸识别系统） ······························· 22

2.8 深度学习的硬件支撑——NVIDIA GPU ············································ 23

本章参考文献 ······························································································ 24

深度学习理论篇

第3 章 神经网络 ························································································· 30

3.1 神经元的概念 ······················································································ 30

3.2 神经网络 ····························································································· 31

3.2.1 后向传播算法 ··········································································· 32

3.2.2 后向传播算法推导 ··································································· 33

3.3 神经网络算法示例 ·············································································· 36

本章参考文献 ······························································································ 38

第4 章 卷积神经网络 ················································································· 39

4.1 卷积神经网络特性 ················································································ 39

4.1.1 局部连接 ·················································································· 40

4.1.2 权值共享 ·················································································· 41

4.1.3 空间相关下采样 ······································································· 42

4.2 卷积神经网络操作 ·············································································· 42

4.2.1 卷积操作 ·················································································· 42

4.2.2 下采样操作 ·············································································· 44

4.3 卷积神经网络示例：LeNet-5 ····························································· 45

本章参考文献 ······························································································ 48

深度学习工具篇

第5 章 深度学习工具Caffe ········································································ 50

5.1 Caffe 的安装 ························································································ 50

5.1.1 安装依赖包 ·············································································· 51

5.1.2 CUDA 安装 ·············································································· 51

5.1.3 MATLAB 和Python 安装 ························································ 54

5.1.4 OpenCV 安装（可选） ···························································· 59

5.1.5 Intel MKL 或者BLAS 安装 ····················································· 59

5.1.6 Caffe 编译和测试 ····································································· 59

5.1.7 Caffe 安装问题分析 ································································· 62

5.2 Caffe 框架与源代码解析 ···································································· 63

5.2.1 数据层解析 ·············································································· 63

5.2.2 网络层解析 ·············································································· 74

5.2.3 网络结构解析 ··········································································· 92

5.2.4 网络求解解析 ········································································· 104

本章参考文献 ···························································································· 109

第6 章 深度学习工具Pylearn2 ································································ 110

6.1 Pylearn2 的安装 ·················································································· 110

6.1.1 相关依赖安装 ·········································································· 110

6.1.2 安装Pylearn2 ·········································································· 112

6.2 Pylearn2 的使用 ·················································································· 112

本章参考文献 ····························································································· 116

深度学习实践篇（入门与进阶）

第7 章 基于深度学习的手写数字识别 ······················································ 118

7.1 数据介绍 ···························································································· 118

7.1.1 MNIST 数据集 ········································································ 118

7.1.2 提取MNIST 数据集图片 ······················································· 120

7.2 手写字体识别流程 ············································································ 121

7.2.1 模型介绍 ················································································ 121

7.2.2 操作流程 ················································································ 126

7.3 实验结果分析 ···················································································· 127

本章参考文献 ···························································································· 128

第8 章 基于深度学习的图像识别 ····························································· 129

8.1 数据来源 ··························································································· 129

8.1.1 Cifar10 数据集介绍 ································································ 129

8.1.2 Cifar10 数据集格式 ································································ 129

8.2 Cifar10 识别流程 ··············································································· 130

8.2.1 模型介绍 ················································································ 130

8.2.2 操作流程 ················································································ 136

8.3 实验结果分析 ······················································································ 139

本章参考文献 ···························································································· 140

第9 章 基于深度学习的物体图像识别 ······················································ 141

9.1 数据来源 ··························································································· 141

9.1.1 Caltech101 数据集 ·································································· 141

9.1.2 Caltech101 数据集处理 ·························································· 142

9.2 物体图像识别流程 ············································································ 143

9.2.1 模型介绍 ················································································ 143

9.2.2 操作流程 ················································································ 144

9.3 实验结果分析 ···················································································· 150

本章参考文献 ···························································································· 151

第10 章 基于深度学习的人脸识别 ··························································· 152

10.1 数据来源 ························································································· 152

10.1.1 AT＆T Facedatabase 数据库 ·················································· 152

10.1.2 数据库处理 ··········································································· 152

10.2 人脸识别流程 ·················································································· 154

10.2.1 模型介绍 ·············································································· 154

10.2.2 操作流程 ·············································································· 155

10.3 实验结果分析 ·················································································· 159

本章参考文献 ···························································································· 160

深度学习实践篇（高级应用）

第11 章 基于深度学习的人脸识别——DeepID 算法 ································ 162

11.1 问题定义与数据来源 ······································································ 162

11.2 算法原理 ·························································································· 163

11.2.1 数据预处理 ··········································································· 163

11.2.2 模型训练策略 ······································································· 164

11.2.3 算法验证和结果评估 ··························································· 164

11.3 人脸识别步骤 ·················

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