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深度学习
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MATLAB计算机视觉与深度学习实战-赠在线交流卡和本书源码
深度学习原理与应用实践 版权信息
- ISBN：9787121304132
- 条形码：9787121304132 ; 978-7-121-30413-2
- 装帧：暂无
- 册数：暂无
- 重量：暂无
- 所属分类：>
深度学习原理与应用实践 本书特色
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。
深度学习原理与应用实践 内容简介
本书全面、系统地介绍深度学习相关的技术，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。
深度学习原理与应用实践 目录
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
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
展开全部
深度学习原理与应用实践 作者简介
张重生，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，ERCIM/Marie-Curie Fellow。
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