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| 內容簡介： |
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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。
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| 關於作者： |
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张重生，男，博士，教授，硕士生导师，河南大学大数据研究中心、大数据团队带头人。研究领域为大数据分析、深度学习、数据挖掘、数据库、数据流（实时数据分析）。博士毕业于 INRIA,France法国国家信息与自动化研究所，获得优秀博士论文荣誉。2010年08月至2011年3月，在美国加州大学洛杉矶分校UCLA，计算机系，师从著名的数据库专家Carlo Zaniolo教授，从事数据挖掘领域的合作研究。 2012-2013,挪威科技大学，ERCIMMarie-Curie Fellow。
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| 目錄：
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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 ·······································4
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