深度学习与神经网络

定　　价：86 元

• 作者：赵金晶 等
• 出版时间：2024/2/1
• ISBN：9787121473739
• 出 版 社：电子工业出版社

• 中图法分类：TP18 
• 页码：232
• 版次：01
• 开本：16开

内容简介

全书分为7 个章节。第1 章绪论，梳理了人工智能不同技术流派的特点、深度学习的发展及前沿技术；第2 章介绍相关预备知识，包括线性代数、概率论、优化理论以及机器学习的基础知识；第3 章从前馈神经网络的基础模型——感知器出发，介绍前馈神经网络的基本结构以及涉及的激活函数、梯度下降、反向传播等内容；第4 章，介绍深度模型的优化问题，讨论了神经网络优化中常见的病态问题；第5 章介绍深度学习中的正则化方法，包括范数惩罚、数据集增强与噪声注入、提前停止等；第6 章介绍了卷积神经网络，以及卷积神经网络在计算机视觉领域的具体应用；第7 章通过实际案例介绍循环神经网络与卷积神经网络的结合应用。

作者介绍

赵金晶，女，1981年生，军事科学院系统工程研究院高级工程师，国防科技大学计算机学院博士毕业，主要研究方向为网络与信息安全、人工智能技术。先后承担国家自然科学基金、国家973计划、863计划等重大项目，曾获国家自然科学基金青年基金资助。获省部级科技进步奖二等奖5项、三等奖1项，发明专利20余项。发表学术论文80余篇，出版学术著作2部。电子邮箱：[e-mail]李虎，男，1987年生，军事科学院系统工程研究院工程师，国防科技大学计算机学院博士毕业，主要研究方向为网络与信息安全。先后承担、参与国家973计划、863计划、装备预研等各类科研项目10余项。获省部级科技进步奖二等奖2项、发明专利10余项，发表论文20余篇。电子邮箱：[e-mail]张明，男，1990年生，军事科学院系统工程研究院工程师，北京系统工程研究所硕士毕业，主要研究方向为机器学习和人工智能安全。先后参与、主持国家自然科学基金、重点实验室基金、国家973重大项目、装备预研和国防科技创新特区等各类科研项目10余项。获省部级科技进步奖一等奖1项，二等奖2项。发表学术论文20余篇，其中SCI检索5篇，EI检索10余篇。电子邮箱：[e-mail]

目 录

第1 章 绪论····················································································.1
1.1 人工智能·············································································.2
1.1.1 人工智能技术的发展历程···············································.3
1.1.2 人工智能技术的流派·····················································.9
1.2 深度学习与神经网络概述······················································.11
1.2.1 深度学习与神经网络技术的发展历程······························.11
1.2.2 深度学习与神经网络的前沿技术····································.16
1.3 深度学习系统架构·······························································.17
1.4 深度学习框架·····································································.19
1.5 深度学习的应用··································································.20
1.5.1 计算机视觉·······························································.20
1.5.2 语音语义··································································.21
1.5.3 自然语言处理····························································.22
1.6 人工智能潜在的安全风险······················································.22
1.6.1 数据层面的风险·························································.23
1.6.2 算法模型层面的风险···················································.23
1.6.3 智能计算框架层面的风险·············································.23
1.6.4 基础软硬件层面的风险················································.24
1.6.5 应用服务层面的风险···················································.24
本章小结··················································································.24
第2 章 预备知识············································································.25
2.1 相关数学基础·····································································.25
2.1.1 线性代数··································································.25
2.1.2 概率论·····································································.27
2.1.3 优化理论··································································.32
2.2 机器学习基础·····································································.34
2.2.1 机器学习算法的基本流程·············································.35
2.2.2 机器学习常用评价指标················································.36
2.2.3 典型机器学习算法······················································.41
2.3 实验环境基础·····································································.49
2.3.1 GPU 驱动的安装配置··················································.49
2.3.2 依赖环境的安装配置···················································.52
2.3.3 深度学习框架的安装配置·············································.54
2.3.4 集成开发环境的安装配置·············································.56
本章小结··················································································.57
第3 章 前馈神经网络······································································.58
3.1 感知器··············································································.58
3.1.1 单层感知器·······························································.58
3.1.2 多层感知器·······························································.61
3.1.3 前馈神经网络的基本结构·············································.62
3.2 激活函数···········································································.63
3.2.1 Sigmoid 函数·····························································.63
3.2.2 ReLU 函数································································.65
3.2.3 Tanh 函数·································································.67
3.2.4 Softmax 函数·····························································.68
3.3 误差反向传播·····································································.69
3.3.1 梯度下降法·······························································.69
3.3.2 链式法则··································································.70
3.3.3 反向传播··································································.72
本章小结··················································································.75
第4 章 深度模型的优化···································································.76
4.1 神经网络的优化问题····························································.76
4.1.1 局部最优和振荡陷阱···················································.76
4.1.2 梯度爆炸和梯度消失···················································.78
4.2 常见的优化算法··································································.80
4.2.1 梯度下降优化算法······················································.80
4.2.2 二阶优化算法····························································.81
4.3 自适应学习率算法·······························································.85
4.3.1 AdaGrad 算法····························································.85
4.3.2 RMSprop 算法···························································.86
4.3.3 Adam 算法································································.86
4.4 参数初始化方法··································································.87
4.4.1 随机初始化·······························································.87
4.4.2 Xavier 初始化····························································.88
4.4.3 He 初始化·································································.89
本章小结··················································································.90
第5 章 深度学习中的正则化·····························································.91
5.1 范数惩罚···········································································.91
5.1.1 L1 正则化··································································.91
5.1.2 L2 正则化··································································.93
5.2 数据集增强与噪声注入·························································.94
5.2.1 数据集增强·······························································.94
5.2.2 噪声注入··································································.95
5.3 提前停止···········································································.97
5.4 Dropout ·············································································.97
5.5 批归一化···········································································.99
本章小结·················································································.102
第6 章 卷积神经网络······································································103
6.1 卷积神经网络的发展历程·····················································.103
6.2 卷积神经网络的基本组成·····················································.104
6.2.1 卷积层····································································.105
6.2.2 池化层····································································.106
6.2.3 全连接层·································································.108
6.3 常见卷积神经网络结构························································.108
6.3.1 VGG 网络································································.109
6.3.2 GoogLeNet 网络························································.111
6.3.3 ResNet 网络·····························································.117
6.4 深度生成网络····································································.122
6.4.1 生成对抗网络···························································.122
6.4.2 深度卷积生成对抗网络···············································.123
6.5 图像分类案例····································································.134
6.5.1 步骤1：搭建环境······················································.135
6.5.2 步骤2：导入依赖库···················································.137
6.5.3 步骤3：获取数据······················································.137
6.5.4 步骤4：定义AlexNet 网络··········································.138
6.5.5 步骤5：模型初始化···················································.140
6.5.6 步骤6：模型训练······················································.140
6.6 目标检测案例····································································.143
6.6.1 步骤1：环境配置和模型下载·······································.144
6.6.2 步骤2：主函数解析···················································.151
6.6.3 步骤3：终端指令运行················································.152
本章小结·················································································.153
第7 章 循环神经网络·····································································.154
7.1 循环神经网络的基本原理·····················································.154
7.1.1 循环神经网络的原理··················································.154
7.1.2 双向循环神经网络·····················································.157
7.2 循环神经网络在实际中的应用···············································.158
7.2.1 文本生成·································································.159
7.2.2 语音识别·································································.159
7.2.3 机器翻译·································································.160
7.2.4 生成图像描述···························································.161
7.2.5 视频动作检测···························································.162
7.2.6 信号分类·································································.162
7.3 长短期记忆网络及其他门控循环神经网络································.163
7.3.1 长短期记忆网络························································.164
7.3.2 其他门控循环神经网络···············································.166
7.4 深度学习在文本和序列中的应用············································.167
7.4.1 文本数据处理···························································.167
7.4.2 文本分类和情感分析··················································.180
7.4.3 机器翻译·································································.180
7.4.4 命名实体识别···························································.182
7.5 卷积神经网络与循环神经网络···············································.183
7.5.1 卷积神经网络与循环神经网络的对比·····························.183
7.5.2 卷积神经网络与循环神经网络的组合应用·······················.184
7.6 案例：深度学习的诗歌生成··················································.185
7.6.1 步骤1：导入依赖库···················································.186
7.6.2 步骤2：读取数据······················································.187
7.6.3 步骤3：构造数据集···················································.188
7.6.4 步骤4：构造模型······················································.190
7.6.5 步骤5：训练过程······················································.192
7.6.6 步骤6：生成文本······················································.195
7.7 案例：基于LSTM 算法的股票预测·········································.196
7.7.1 步骤1：导入依赖库···················································.196
7.7.2 步骤2：获取并处理数据·············································.197
7.7.3 步骤3：构建预测数据序列··········································.199
7.7.4 步骤4：构建LSTM 网络············································.201
7.7.5 步骤5：训练网络······················································.202
7.7.6 步骤6：预测测试集···················································.203
7.8 案例：基于深度学习的文本分类············································.204
7.8.1 步骤1：项目入口······················································.205
7.8.2 步骤2：训练模块······················································.207
7.8.3 步骤3：验证和测试函数·············································.210
7.8.4 步骤4：数据预处理模块·············································.211
7.8.5 步骤5：定义模型······················································.215
7.8.6 步骤6：分类结果展示················································.218
本章小结·················································································.219