《深度学习：原理与应用实践.pdf》PDF高清完整版-免费下载
《深度学习：原理与应用实践.pdf》PDF高清完整版-免费下载
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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 Torch ···················································································· 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
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