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| 內容簡介： |
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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。
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| 關於作者： |
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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：zhjj0420@126.com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：lihu@nudt.edu.cn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：zm_stiss@163.com
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| 目錄：
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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·····································
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