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新書推薦： 《 津轻：日本无赖派文学代表太宰治自传性随笔集 》 售價：NT$ 302.0 《 河流之齿 》 售價：NT$ 270.0 《 新经济史革命：计量学派与新制度学派 》 售價：NT$ 406.0 《 盗墓笔记之秦岭神树4 》 售價：NT$ 259.0 《 战胜人格障碍 》 售價：NT$ 302.0 《 逃不开的科技创新战争 》 售價：NT$ 467.0 《 漫画三国一百年 》 售價：NT$ 250.0 《 希腊文明3000年（古希腊的科学精神，成就了现代科学之源） 》 售價：NT$ 374.0

建議一齊購買： + NT$ 437 《 “中期鲁迅”研究 》 + NT$ 435 《 新世纪高等院校英语专业本科生系列教材（修订版）：综合教程（第3版）增强版 第3册 学生用书 》 + NT$ 549 《 全国大学生数学竞赛解析教程（非数学专业类）（上下册） 》 + NT$ 270 《 职业生涯规划与就业创业指导（第2版）（21世纪通识教育系列教材） 》 + NT$ 510 《 害虫生物防治的原理和方法（第三版） 》 + NT$ 347 《 大数据技术原理与应用 ——概念、存储、处理、 分析与应用（第3版） 》 內容簡介： 随着“十四五”规划纲要中提出“协同发展云服务与边缘计算服务”的观点，边云智能已成为未来发展的重要趋势。本书依托于政策大背景，旨在向读者介绍边云智能的基础知识和应用。书中分为四个篇章，第一篇章介绍了边云架构的骨架和基础概念，第二篇章介绍了人工智能算法和深度学习模型，第三篇章介绍了云端决策算法和边缘端轻量化算法，第四篇章介绍了边云智能在智慧教育领域的应用。本书可以使读者了解边云计算的基本概念和原理逻辑，熟悉基本的人工智能计算方法和数据分析的逻辑和运用场景。通过数据科学的思路和方法，读者可以将无人系统的数据智能化应用提升，并培养数据导向思维方式，为未来学习智能无人系统科学与技术学科打下基础。 本书目标明确，技术先进，强调课程思政和润物无声的教育理念，旨在提高学生的数据科学素养和“用数据”的能力。本书面向智能无人系统科学与技术专业的研究生，涵盖人工智能、大数据分析、数据挖掘和边云计算等学科，具有交叉性的特点。同时，资深从业者也可将其作为参考书籍。 關於作者： 沈钧戈，西北工业大学助理教授，陕西省电子学会图形图象专委会委员，主讲课程为智慧城市与计算机视觉，并负责相关慕课建设。 目錄： 第 1 章 绪论 ····························································································1 1.1 边云智能产生的大背景····································································1 1.1.1 新一代信息技术的快速发展·····················································2 1.1.2 国家政策的支持与引导···························································6 1.2 边云智能······················································································7 1.3 边云智能的发展·············································································9 1.3.1 边云智能的三大发展阶段························································9 1.3.2 城市大脑··········································································.11 1.4 “智能+”新潮头··········································································.13 1.4.1 “智能+”技术新融合···························································.13 1.4.2 多维度场景应用·································································.14 本章习题··························································································.15 第 2 章 边云架构 ···················································································.16 2.1 系统工程方法论··········································································.17 2.1.1 概述 ················································································.17 2.1.2 基本方法··········································································.17 2.2 边云智能体系架构模型·································································.20 2.2.1 概念框架··········································································.20 2.2.2 层次结构··········································································.22 2.3 协同模式···················································································.23 2.3.1 “云-边”协同 ····································································.24 2.3.2 “边-边”协同 ····································································.25 2.3.3 “边-端”协同 ····································································.27 2.3.4 “云-边-端”协同 ································································.28 2.3.5 度量指标··········································································.28 2.4 边云智能架构应用·······································································.30 2.4.1 “云-边-端”区块链 ·····························································.30 2.4.2 “云-边-端”一体化机器人系统 ··············································.32 本章习题··························································································.33 第 3 章 深度学习 ···················································································.35 3.1 深度学习概念·············································································.36 3.1.1 人工智能与机器学习···························································.36 3.1.2 深度学习··········································································.37 3.1.3 神经网络··········································································.39 3.2 前馈神经网络·············································································.39 3.2.1 感知机模型·······································································.39 3.2.2 反向传播··········································································.42 3.2.3 卷积神经网络····································································.44 3.2.4 几种典型的卷积神经网络·····················································.47 3.3 反馈神经网络·············································································.50 3.3.1 循环神经网络····································································.50 3.3.2 长短期神经网络·································································.53 3.4 Transformer 神经网络 ···································································.56 3.4.1 编码器单元与解码器单元·····················································.58 3.4.2 多头注意力机制·································································.59 3.4.3 非参位置编码····································································.60 本章习题··························································································.61 第 4 章 自然语言处理 ··············

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