深度学习：原理与应用实践

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 ·················································································.165 11.3.1 数据预处理 ··········································································.165 11.3.2 深度网络结构模型 ······························································.168 11.3.3 提取深度特征与人脸验证 ··················································.171 11.4 实验结果分析 ·················································································.174 11.4.1 实验数据 ··············································································.174 11.4.2 实验结果分析 ······································································.175 本章参考文献 ···························································································.176 第12 章 基于深度学习的表情识别 ··························································.177 12.1 表情数据 ························································································.177 12.1.1 Cohn-Kanade（CK+）数据库 ············································.177 12.1.2 JAFFE 数据库 ·····································································.178 12.2 算法原理 ························································································.179 12.3 表情识别步骤 ·················································································.180 12.3.1 数据预处理 ··········································································.180 12.3.2 深度神经网络结构模型 ······················································.181 12.3.3 提取深度特征及分类 ··························································.182 12.4 实验结果分析 ·················································································.184 12.4.1 实现细节 ·············································································.184 12.4.2 实验结果对比 ······································································.185 本章参考文献 ···························································································.188 第13 章 基于深度学习的年龄估计 ··························································.190 13.1 问题定义 ························································································.190 13.2 年龄估计算法 ·················································································.190 13.2.1 数据预处理 ··········································································.190 13.2.2 提取深度特征 ······································································.192 13.2.3 提取LBP 特征 ····································································.196 13.2.4 训练回归模型 ······································································.196 13.3 实验结果分析 ·················································································.199 本章参考文献 ···························································································.199 第14 章 基于深度学习的人脸关键点检测 ···············································.200 14.1 问题定义和数据来源 ·····································································.200 14.2 基于深度学习的人脸关键点检测的步骤 ······································.201 14.2.1 数据预处理 ··········································································.201 14.2.2 训练深度学习网络模型 ······················································.206 14.2.3 预测和处理关键点坐标 ······················································.207 本章参考文献 ···························································································.212 深度学习总结与展望篇 第15 章 总结与展望 ················································································.214 15.1 深度学习领域当前的主流技术及其应用领域 ······························.214 15.1.1 图像识别 ·············································································.214 15.1.2 语音识别与自然语言理解 ··················································.215 15.2 深度学习的缺陷 ·············································································.215 15.2.1 深度学习在硬件方面的门槛较高 ·······································.215 15.2.2 深度学习在软件安装与配置方面的门槛较高 ···················.216 15.2.3 深度学习最重要的问题在于需要海量的有标注的数据作为支撑 ··.216 15.2.4 深度学习的最后阶段竟然变成枯燥、机械、及其耗时的调参工作 ··.217 15.2.5 深度学习不适用于数据量较小的数据 ·······························.218 15.2.6 深度学习目前主要用于图像、声音的识别和自然语言的理解 ····.218 15.2.7 研究人员从事深度学习研究的困境 ···································.219 15.3 展望 ································································································.220 本章参考文献 ···························································································.220