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书名：创新工场讲AI课：从知识到实践
定价：89.0
ISBN：9787121408458
作者：创新工场DeeCamp组委会
版次：第1版
出版时间：2021-04
内容提要：
创新工场于 2017 年发起了面向高校在校生的DeeCamp 人工智能训练营（简称DeeCamp训练营），训练营内容涵盖学术界与产业界领军人物带来的全新AI 知识体系和来自产业界的真实实践课题，旨在提升高校AI 人才在行业应用中的实践能力，以及推进产学研深度结合。 本书以近两年 DeeCamp 训练营培训内容为基础，精选部分导师的授课课程及有代表性的学员参赛项目，以文字形式再现训练营"知识课程+产业实战”的教学模式和内容。全书共分为9 章，第1 章、第2 章分别介绍AI 赋能时代的创业、AI 的产品化和工程化挑战；第3 章至第8 章聚焦于AI 理论与产业实践的结合，内容涵盖机器学习、自然语言处理、计算机视觉、深度学习模型的压缩与加速等；第9 章介绍了 4 个优秀实践课题，涉及自然语言处理和计算机视觉两个方向。
作者简介：
DeeCamp 人工智能训练营由创新工场于 2017 年发起，是一个致力于培养人工智能应用型人才的公益项目。2018 年 DeeCamp 被***选中作为「中国高校人工智能人才国际培养计划」两个组成部分之一的学生培训营。现已初步建立了以创造性的团队工程实践项目为主干，以打通学术、产业边界的系统性知识培训为支撑，聚焦未来科技变革与商业发展，成规模、可复制的人工智能应用型人才培养体系。
目录：
第1 章AI 赋能时代的创业······················································································1
1.1 中国AI 如何弯道超车····································································································2
1.2 AI 从“发明期”进入“应用期”··················································································9
1.2.1 深度学习助推AI 进入“应用期”···································································10
1.2.2 To B 创业迎来黄金发展期···············································································.11
1.2.3 “传统产业+AI”将创造巨大价值·····································································14
1.2.4 AI 赋能传统行业四部曲···················································································16
1.3 AI 赋能时代的创业特点·······························································································21
1.3.1 海外科技巨头成功因素解析·············································································21
1.3.2 科学家创业的优势和短板·················································································24
1.3.3 四因素降低AI 产品化、商业化门槛·······························································26
1.4 给未来AI 人才的建议··································································································30
第2 章AI 的产品化和工程化挑战·········································································35
2.1 从AI 科研到AI 商业化································································································36
2.2 产品经理视角—数据驱动的产品研发······································································40
2.2.1 数据驱动············································································································41
2.2.2 典型C 端产品的设计和管理············································································43
2.2.3 典型B 端产品解决方案的设计和管理·····························································46
2.2.4 AI 技术的产品化·······························································································48
2.3 架构设计师视角—典型AI 架构···············································································51
2.3.1 为什么要重视系统架构····················································································51
2.3.2 与AI 相关的典型系统架构··············································································53
2.4 写在本章*后的几句话································································································78
本章参考文献 ························································································································79
第3 章机器学习的发展现状及前沿进展 ······························································81
3.1 机器学习的发展现状····································································································82
3.2 机器学习的前沿进展····································································································85
3.2.1 复杂模型············································································································85
3.2.2 表示学习············································································································90
3.2.3 自动机器学习····································································································95
第4 章自然语言理解概述及主流任务 ··································································99
4.1 自然语言理解概述······································································································100
4.2 NLP 主流任务·············································································································100
4.2.1 中文分词··········································································································101
4.2.2 指代消解··········································································································102
4.2.3 文本分类··········································································································103
4.2.4 关键词（短语）的抽取与生成·······································································105
4.2.5 文本摘要··········································································································107
4.2.6 情感分析··········································································································108
本章参考文献·····················································································································.111
第 5 章机器学习在 NLP 领域的应用及产业实践···············································115
5.1 自然语言句法分析·····································································································.116
5.1.1 自然语言句法分析的含义与背景··································································.116
5.1.2 研究句法分析的几个要素··············································································.117
5.1.3 句法分析模型举例··························································································121
5.2 深度学习在句法分析模型参数估计中的应用····························································125
5.2.1 符号嵌入··········································································································126
5.2.2 上下文符号嵌入······························································································129
本章参考文献······················································································································131
第 6 章计算机视觉前沿进展及实践 ····································································133
6.1 计算机视觉概念··········································································································134
6.2 计算机视觉认知过程··································································································136
6.2.1 从低层次到高层次的理解···············································································137
6.2.2 基本任务及主流任务······················································································138
6.3 计算机视觉技术的前沿进展·······················································································141
6.3.1 图像分类任务··································································································141
6.3.2 目标检测任务··································································································148
6.3.3 图像分割任务··································································································151
6.3.4 主流任务的前沿进展······················································································155
6.4 基于机器学习的计算机视觉实践···············································································164
6.4.1 目标检测比赛··································································································164
6.4.2 蛋筒质检··········································································································167
6.4.3 智能货柜··········································································································170
本章参考文献······················································································································173
第 7 章深度学习模型压缩与加速的技术发展与应用·········································175
7.1 深度学习的应用领域及面临的挑战···········································································176
7.1.1 深度学习的应用领域······················································································176
7.1.2 深度学习面临的挑战······················································································178
7.2 深度学习模型的压缩和加速方法···············································································180
7.2.1 主流压缩和加速方法概述···············································································180
7.2.2 权重剪枝··········································································································182
7.2.3 权重量化··········································································································192
7.2.4 知识蒸馏··········································································································199
7.2.5 权重量化与权重剪枝结合并泛化···································································200
7.3 模型压缩与加速的应用场景·······················································································201
7.3.1 驾驶员安全检测系统······················································································202
7.3.2 高级驾驶辅助系统··························································································202
7.3.3 车路协同系统··································································································203
本章参考文献······················································································································204
第 8 章终端深度学习基础、挑战和工程实践·····················································207
8.1 终端深度学习的技术成就及面临的核心问题····························································208
8.1.1 终端深度学习的技术成就···············································································208
8.1.2 终端深度学习面临的核心问题·······································································209
8.2 在冗余条件下减少资源需求的方法··········································································.211
8.3 在非冗余条件下减少资源需求的方法·······································································213
8.3.1 特殊化模型······································································································214
8.3.2 动态模型··········································································································215
8.4 深度学**统的设计··································································································216
8.4.1 实际应用场景中的挑战··················································································216
8.4.2 实际应用场景中的问题解决···········································································217
8.4.3 案例分析··········································································································219
本章参考文献······················································································································224
第 9 章DeeCamp 训练营*佳商业项目实战·······················································225
9.1 方仔照相馆—AI 辅助单张图像生成积木方头仔···················································227
9.1.1 让“AI 方头仔”触手可及·············································································227
9.1.2 理论支撑：BiSeNet 和Mask R-CNN ·····························································229
9.1.3 任务分解：从图像分析到积木生成的实现····················································231
9.1.4 团队协作与时间安排······················································································237
9.2 AI 科幻世界—基于预训练语言模型的科幻小说生成系统····································242
9.2.1 打造人机协作的科幻小说作家·······································································242
9.2.2 理论支撑：语言模型、Transformer 模型和GPT2 预训练模型·····················243
9.2.3 从“找小说”到“写小说”的实现步骤························································247
9.2.4 团队协作与时间安排······················································································250
9.3 宠物健康识别—基于图像表征学习的宠物肥胖度在线检测系统·························254
9.3.1 人人都能做“养宠达人”···············································································254
9.3.2 理论支撑：表征学习、人脸识别原理和ArcFace 损失函数·························257
9.3.3 任务分解：从数据收集到肥胖度检测···························································259
9.3.4 团队协作与时间安排······················································································262
9.4 商品文案生成—基于检索和生成的智能文案系统················································265
9.4.1 智能内容生成··································································································265
9.4.2 理论支撑：Word2Vec 词嵌入、预训练语言模型BERT 和Seq2Seq
文本生成··········································································································266
9.4.3 任务分解：“寻章摘句”和“文不加点”······················································269
9.4.4 团队协作与时间安排······················································································273
本章参考文献······················································································································276
定价：89.0
ISBN：9787121408458
作者：创新工场DeeCamp组委会
版次：第1版
出版时间：2021-04
内容提要：
创新工场于 2017 年发起了面向高校在校生的DeeCamp 人工智能训练营（简称DeeCamp训练营），训练营内容涵盖学术界与产业界领军人物带来的全新AI 知识体系和来自产业界的真实实践课题，旨在提升高校AI 人才在行业应用中的实践能力，以及推进产学研深度结合。 本书以近两年 DeeCamp 训练营培训内容为基础，精选部分导师的授课课程及有代表性的学员参赛项目，以文字形式再现训练营"知识课程+产业实战”的教学模式和内容。全书共分为9 章，第1 章、第2 章分别介绍AI 赋能时代的创业、AI 的产品化和工程化挑战；第3 章至第8 章聚焦于AI 理论与产业实践的结合，内容涵盖机器学习、自然语言处理、计算机视觉、深度学习模型的压缩与加速等；第9 章介绍了 4 个优秀实践课题，涉及自然语言处理和计算机视觉两个方向。
作者简介：
DeeCamp 人工智能训练营由创新工场于 2017 年发起，是一个致力于培养人工智能应用型人才的公益项目。2018 年 DeeCamp 被***选中作为「中国高校人工智能人才国际培养计划」两个组成部分之一的学生培训营。现已初步建立了以创造性的团队工程实践项目为主干，以打通学术、产业边界的系统性知识培训为支撑，聚焦未来科技变革与商业发展，成规模、可复制的人工智能应用型人才培养体系。
目录：
第1 章AI 赋能时代的创业······················································································1
1.1 中国AI 如何弯道超车····································································································2
1.2 AI 从“发明期”进入“应用期”··················································································9
1.2.1 深度学习助推AI 进入“应用期”···································································10
1.2.2 To B 创业迎来黄金发展期···············································································.11
1.2.3 “传统产业+AI”将创造巨大价值·····································································14
1.2.4 AI 赋能传统行业四部曲···················································································16
1.3 AI 赋能时代的创业特点·······························································································21
1.3.1 海外科技巨头成功因素解析·············································································21
1.3.2 科学家创业的优势和短板·················································································24
1.3.3 四因素降低AI 产品化、商业化门槛·······························································26
1.4 给未来AI 人才的建议··································································································30
第2 章AI 的产品化和工程化挑战·········································································35
2.1 从AI 科研到AI 商业化································································································36
2.2 产品经理视角—数据驱动的产品研发······································································40
2.2.1 数据驱动············································································································41
2.2.2 典型C 端产品的设计和管理············································································43
2.2.3 典型B 端产品解决方案的设计和管理·····························································46
2.2.4 AI 技术的产品化·······························································································48
2.3 架构设计师视角—典型AI 架构···············································································51
2.3.1 为什么要重视系统架构····················································································51
2.3.2 与AI 相关的典型系统架构··············································································53
2.4 写在本章*后的几句话································································································78
本章参考文献 ························································································································79
第3 章机器学习的发展现状及前沿进展 ······························································81
3.1 机器学习的发展现状····································································································82
3.2 机器学习的前沿进展····································································································85
3.2.1 复杂模型············································································································85
3.2.2 表示学习············································································································90
3.2.3 自动机器学习····································································································95
第4 章自然语言理解概述及主流任务 ··································································99
4.1 自然语言理解概述······································································································100
4.2 NLP 主流任务·············································································································100
4.2.1 中文分词··········································································································101
4.2.2 指代消解··········································································································102
4.2.3 文本分类··········································································································103
4.2.4 关键词（短语）的抽取与生成·······································································105
4.2.5 文本摘要··········································································································107
4.2.6 情感分析··········································································································108
本章参考文献·····················································································································.111
第 5 章机器学习在 NLP 领域的应用及产业实践···············································115
5.1 自然语言句法分析·····································································································.116
5.1.1 自然语言句法分析的含义与背景··································································.116
5.1.2 研究句法分析的几个要素··············································································.117
5.1.3 句法分析模型举例··························································································121
5.2 深度学习在句法分析模型参数估计中的应用····························································125
5.2.1 符号嵌入··········································································································126
5.2.2 上下文符号嵌入······························································································129
本章参考文献······················································································································131
第 6 章计算机视觉前沿进展及实践 ····································································133
6.1 计算机视觉概念··········································································································134
6.2 计算机视觉认知过程··································································································136
6.2.1 从低层次到高层次的理解···············································································137
6.2.2 基本任务及主流任务······················································································138
6.3 计算机视觉技术的前沿进展·······················································································141
6.3.1 图像分类任务··································································································141
6.3.2 目标检测任务··································································································148
6.3.3 图像分割任务··································································································151
6.3.4 主流任务的前沿进展······················································································155
6.4 基于机器学习的计算机视觉实践···············································································164
6.4.1 目标检测比赛··································································································164
6.4.2 蛋筒质检··········································································································167
6.4.3 智能货柜··········································································································170
本章参考文献······················································································································173
第 7 章深度学习模型压缩与加速的技术发展与应用·········································175
7.1 深度学习的应用领域及面临的挑战···········································································176
7.1.1 深度学习的应用领域······················································································176
7.1.2 深度学习面临的挑战······················································································178
7.2 深度学习模型的压缩和加速方法···············································································180
7.2.1 主流压缩和加速方法概述···············································································180
7.2.2 权重剪枝··········································································································182
7.2.3 权重量化··········································································································192
7.2.4 知识蒸馏··········································································································199
7.2.5 权重量化与权重剪枝结合并泛化···································································200
7.3 模型压缩与加速的应用场景·······················································································201
7.3.1 驾驶员安全检测系统······················································································202
7.3.2 高级驾驶辅助系统··························································································202
7.3.3 车路协同系统··································································································203
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第 8 章终端深度学习基础、挑战和工程实践·····················································207
8.1 终端深度学习的技术成就及面临的核心问题····························································208
8.1.1 终端深度学习的技术成就···············································································208
8.1.2 终端深度学习面临的核心问题·······································································209
8.2 在冗余条件下减少资源需求的方法··········································································.211
8.3 在非冗余条件下减少资源需求的方法·······································································213
8.3.1 特殊化模型······································································································214
8.3.2 动态模型··········································································································215
8.4 深度学**统的设计··································································································216
8.4.1 实际应用场景中的挑战··················································································216
8.4.2 实际应用场景中的问题解决···········································································217
8.4.3 案例分析··········································································································219
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第 9 章DeeCamp 训练营*佳商业项目实战·······················································225
9.1 方仔照相馆—AI 辅助单张图像生成积木方头仔···················································227
9.1.1 让“AI 方头仔”触手可及·············································································227
9.1.2 理论支撑：BiSeNet 和Mask R-CNN ·····························································229
9.1.3 任务分解：从图像分析到积木生成的实现····················································231
9.1.4 团队协作与时间安排······················································································237
9.2 AI 科幻世界—基于预训练语言模型的科幻小说生成系统····································242
9.2.1 打造人机协作的科幻小说作家·······································································242
9.2.2 理论支撑：语言模型、Transformer 模型和GPT2 预训练模型·····················243
9.2.3 从“找小说”到“写小说”的实现步骤························································247
9.2.4 团队协作与时间安排······················································································250
9.3 宠物健康识别—基于图像表征学习的宠物肥胖度在线检测系统·························254
9.3.1 人人都能做“养宠达人”···············································································254
9.3.2 理论支撑：表征学习、人脸识别原理和ArcFace 损失函数·························257
9.3.3 任务分解：从数据收集到肥胖度检测···························································259
9.3.4 团队协作与时间安排······················································································262
9.4 商品文案生成—基于检索和生成的智能文案系统················································265
9.4.1 智能内容生成··································································································265
9.4.2 理论支撑：Word2Vec 词嵌入、预训练语言模型BERT 和Seq2Seq
文本生成··········································································································266
9.4.3 任务分解：“寻章摘句”和“文不加点”······················································269
9.4.4 团队协作与时间安排······················································································273
本章参考文献······················································································································276
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