超简单的机器学习——人气讲师为你讲解AI在工作中的应用

定　　价：79.8 元

• 作者：[日]韮原祐介著，李远超译
• 出版时间：2022/6/1
• ISBN：9787515366272
• 出 版 社：中国青年出版社

• 中图法分类：TP181 
• 纸张：胶版纸
• 开本：32开

内容简介

这是一本面向对AI和机器学习的活用感兴趣的经营层、企划部门、事业部门和IT部门等从业人员的书籍。从打消为什么现在应该努力呢这样的疑问开始，到即便对AI和机器学习的前提知识没有了解，也能够理解如何建立项目，怎样创造出成果的方法论。本书旨在作为咨询公司和系统开发公司等寻求外部AI支援的参考书。

编辑推荐

人气讲师来也！不一样的机器学习和人工智能商业应用宝典：丰富案例，通俗讲解，轻松讲解。互动性满满的讲解方式，活泼丰富的体例设计，总结于实际教学经验的知识点讲解方式，对新手应该牢牢掌握的机器学习和AI商业应用知识点进行了全面的总结和详细地讲解，超详细讲解，消灭看不懂。

作者介绍

[ 日] 韮原祐介：

株式会社brainpad AI 商务本部副部长，提供有关机器学习技术等利用数据科学和数字技术改善经营的咨询服务，包括需求预测、图像分析、复制引擎、搜索等机器学习系统服务，为商业成果的经营创造优势。在包括咨询商会在内的国内外企业中，从事了10 年以上有关经营改革支援的工作。

目 录

第1章 开拓今后业务的机器学习

［人工智能现状与本书概要］

1 什么是机器学习项目

［AI优先］

2 了解AI优先的时代背景

［GAFA Microsoft的对策］

3 从企业来看机器学习的策略

［技术进化的意义］

4 机器学习带来的冲击·····························································

［机器寒武纪到来的背景］

5 了解机器学习受到关注的原因

［第四次工业革命］

6 作为国家成长战略的机器学习···········································

［日本企业的对策］

7 日本企业AI对策实况

［所需的人才供求］

8 AI·机器学习所需人才状况

［从事机器学习的意义］

9 从事机器学习产生新的价值················································

专栏·如何在信息爆炸中获取正确的信息···················

第2章 理解机器学习的机制

［机器学习概要］

10 什么是机器学习

［基于规则］

11 基于规则和机器学习的区别·················································

［机器学习的优点］

12 从机器学习中能得到什么

［机器学习的全貌］

13 理解机器学习的分类····························································

［模型构建的流程］

14 了解机器学习的模型构建

［数据的类型和预处理］

15 理解数据和预处理································································

［算法的选择和调整］

16 了解算法的选择·······················································

［深度学习］

17 深度学习的基本机制·····························································

［模型验证和过拟合］

18 评价模型的精度·······································································

［模型的改善］

19 怎样改善模型········································································

专栏·现在的AI和过去的AI有什么不同

第3章 了解机器学习所必需的资源
［必需的资源］

20 推进机器学习项目所需资源·················································

［物理资源概要］

21 机器学习所需的软件和硬件

［编程语言］

22 了解Python的特征

［库］

23 了解机器学习的库·································································

［统计分析软件］

24 帮助机器学习的软件·····························································

［利用云的硬件资源］

25 机器学习所需的硬件资源·····················································

专栏·AI和中国

第4章 确定项目的目标

［项目的全貌］

26 机器学习项目的阶段区分方式·············································

［构思阶段］

27 抓住构思阶段的全貌·····························································

［课题的设定］

28 什么是机器学习项目的课题··········································

［课题的设定］

29 理解什么样的课题可以通过机器学习解决·························

［用于机器学习的数据种类］

30 理解对课题可用的数据··························································

［机器学习系统］

31 理解机器学习系统化的必要性·····································

［课题方案的探讨］

32 考虑机器学习项目的候选课题··············································

［课题的筛选］

33 用期望成果和数据利用的可能性缩小范围························

［设计业务和系统］

34 设计能够应用机器学习的业务和系统······································

［日程的研究与制定］

35 制定机器学习项目的日程······················································

［ 执行体制的探讨］

36 构建机器学习项目的体制······················································

［ROI的估算］

37 估算ROI（投资回报率）

［方案书的写法］

38 了解有效的方案书的写法·····················································

专栏·回答什么问题，解决什么课题

第5章 确立项目的体制

［利用外部的合作伙伴］

39 探讨向外部合作伙伴企业的支援请求

［合作伙伴的选择标准］

40 确定选择合作伙伴的标准······················································116

［利用分析服务公司］

41 向分析服务公司请求帮助·····················································

［利用咨询公司］

42 向咨询公司请求帮助······························································

［活用公司内部人才］

43 确保机器学习项目所需的人才··············································

［与其他公司签约合作］

44 了解合同形式的特征和注意事项·········································

［费用／成本］

45 什么是机器学习系统的费用预算········································

专栏·10年后工作真的会被AI夺走吗

第6章 验证项目

46 实现的可能性

［PoC阶段的全貌］

47 了解构成PoC阶段的任务

［数据评估］

48 如何评价用于机器学习的数据··············································

［实体模型的构建］

49 构建用于验证可行性的模型··················································

［使用已训练好的模型］

50 利用云服务训练好的模型······················································

［验证项目的评估］

51 评估PoC阶段的验证项目

［传感器的验证］

52 安装的传感器来获取数据··············································

专栏·黄瓜农户与深度学习

第7章 实装机器

53 学习系统

［实装阶段的全貌］

54 了解构成实装阶段的任务·····················································

［实装的特异性］

55 机器学习系统与一般系统开发的区别·································

［需求定义的推进方法］

56 机器学习系统的需求定义······················································

［设计与开发的推进方法］

57 机器学习系统的设计与开发··················································

［测试的推进方法］

58 机器学习系统的测试······························································

专栏·创造超人般的AI

第8章 掌握机器学习系统的使用要点

［应用阶段的全貌］

59 机器学习项目特有的应用任务··············································

［KPI的监测］

60 应该定义怎样的KPI

［模型的微调］

61 修正机器学习模型·································································

［系统的应用］

62 应用机器学习系统的课题······················································

专栏·想制作一个打扫整理机器人

第9章 从成功事例中学习机器学习项目

［案例学习①］

63 根据顾客的行为作出反馈的推荐系统·································

［案例学习②］

64 从SNS的投稿图像中分析商品的使用场景

［案例学习③］

65 机器人根据语音请求作出行动··············································

专栏·AI创造的新工作