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邊云智能數(shù)據(jù)分析與應用 讀者對象:本書面向智能無人系統(tǒng)科學與技術專業(yè)的研究生,涵蓋人工智能、大數(shù)據(jù)分析、數(shù)據(jù)挖掘和邊云計算等學科,具有交叉性的特點。同時,資深從業(yè)者也可將其作為參考書籍。
隨著“十四五”規(guī)劃綱要中提出“協(xié)同發(fā)展云服務與邊緣計算服務”的觀點,邊云智能已成為未來發(fā)展的重要趨勢。本書依托于政策大背景,旨在向讀者介紹邊云智能的基礎知識和應用。書中分為四個篇章,第一篇章介紹了邊云架構的骨架和基礎概念,第二篇章介紹了人工智能算法和深度學習模型,第三篇章介紹了云端決策算法和邊緣端輕量化算法,第四篇章介紹了邊云智能在智慧教育領域的應用。本書可以使讀者了解邊云計算的基本概念和原理邏輯,熟悉基本的人工智能計算方法和數(shù)據(jù)分析的邏輯和運用場景。通過數(shù)據(jù)科學的思路和方法,讀者可以將無人系統(tǒng)的數(shù)據(jù)智能化應用提升,并培養(yǎng)數(shù)據(jù)導向思維方式,為未來學習智能無人系統(tǒng)科學與技術學科打下基礎。 本書目標明確,技術先進,強調(diào)課程思政和潤物無聲的教育理念,旨在提高學生的數(shù)據(jù)科學素養(yǎng)和“用數(shù)據(jù)”的能力。本書面向智能無人系統(tǒng)科學與技術專業(yè)的研究生,涵蓋人工智能、大數(shù)據(jù)分析、數(shù)據(jù)挖掘和邊云計算等學科,具有交叉性的特點。同時,資深從業(yè)者也可將其作為參考書籍。
沈鈞戈,西北工業(yè)大學助理教授,陜西省電子學會圖形圖象專委會委員,主講課程為智慧城市與計算機視覺,并負責相關慕課建設。
第 1 章 緒論 ····························································································1
1.1 邊云智能產(chǎn)生的大背景····································································1 1.1.1 新一代信息技術的快速發(fā)展·····················································2 1.1.2 國家政策的支持與引導···························································6 1.2 邊云智能······················································································7 1.3 邊云智能的發(fā)展·············································································9 1.3.1 邊云智能的三大發(fā)展階段························································9 1.3.2 城市大腦··········································································.11 1.4 “智能+”新潮頭··········································································.13 1.4.1 “智能+”技術新融合···························································.13 1.4.2 多維度場景應用·································································.14 本章習題··························································································.15 第 2 章 邊云架構 ···················································································.16 2.1 系統(tǒng)工程方法論··········································································.17 2.1.1 概述 ················································································.17 2.1.2 基本方法··········································································.17 2.2 邊云智能體系架構模型·································································.20 2.2.1 概念框架··········································································.20 2.2.2 層次結構··········································································.22 2.3 協(xié)同模式···················································································.23 2.3.1 “云-邊”協(xié)同 ····································································.24 2.3.2 “邊-邊”協(xié)同 ····································································.25 2.3.3 “邊-端”協(xié)同 ····································································.27 2.3.4 “云-邊-端”協(xié)同 ································································.28 2.3.5 度量指標··········································································.28 2.4 邊云智能架構應用·······································································.30 2.4.1 “云-邊-端”區(qū)塊鏈 ·····························································.30 2.4.2 “云-邊-端”一體化機器人系統(tǒng) ··············································.32 本章習題··························································································.33 第 3 章 深度學習 ···················································································.35 3.1 深度學習概念·············································································.36 3.1.1 人工智能與機器學習···························································.36 3.1.2 深度學習··········································································.37 3.1.3 神經(jīng)網(wǎng)絡··········································································.39 3.2 前饋神經(jīng)網(wǎng)絡·············································································.39 3.2.1 感知機模型·······································································.39 3.2.2 反向傳播··········································································.42 3.2.3 卷積神經(jīng)網(wǎng)絡····································································.44 3.2.4 幾種典型的卷積神經(jīng)網(wǎng)絡·····················································.47 3.3 反饋神經(jīng)網(wǎng)絡·············································································.50 3.3.1 循環(huán)神經(jīng)網(wǎng)絡····································································.50 3.3.2 長短期神經(jīng)網(wǎng)絡·································································.53 3.4 Transformer 神經(jīng)網(wǎng)絡 ···································································.56 3.4.1 編碼器單元與解碼器單元·····················································.58 3.4.2 多頭注意力機制·································································.59 3.4.3 非參位置編碼····································································.60 本章習題··························································································.61 第 4 章 自然語言處理 ·············································································.62 4.1 自然語言處理概述·······································································.63 4.1.1 自然語言處理簡介······························································.63 4.1.2 自然語言處理的發(fā)展歷史·····················································.74 4.1.3 自然語言處理的應用及面臨的挑戰(zhàn)·········································.76 4.2 文本挖掘···················································································.79 4.2.1 文本挖掘簡介····································································.79 4.2.2 文本挖掘算法····································································.81 4.3 機器翻譯···················································································.87 4.3.1 機器翻譯簡介····································································.87 4.3.2 機器翻譯算法····································································.89 4.4 自動問答系統(tǒng)·············································································.93 4.4.1 自動問答系統(tǒng)簡介······························································.93 4.4.2 自動問答系統(tǒng)模型······························································.95 4.5 語音識別···················································································101 4.5.1 語音識別簡介····································································102 4.5.2 語音識別算法····································································103 本章習題··························································································105 第 5 章 計算機視覺 ················································································107 5.1 計算機視覺概述··········································································107 5.1.1 計算機視覺簡介·································································108 5.1.2 計算機視覺的發(fā)展歷史························································109 5.1.3 計算機視覺的應用及面臨的挑戰(zhàn)···········································.110 5.2 圖像分類··················································································.114 5.2.1 圖像分類簡介···································································.114 5.2.2 圖像分類算法···································································.115 5.3 目標檢測··················································································.119 5.3.1 目標檢測簡介···································································.119 5.3.2 目標檢測算法····································································120 5.4 圖像分割···················································································123 5.4.1 圖像分割簡介····································································123 5.4.2 圖像分割算法····································································124 5.5 目標跟蹤···················································································125 5.5.1 目標跟蹤簡介····································································126 5.5.2 目標跟蹤算法····································································126 本章習題··························································································128 第 6 章 邊緣輕量化 ················································································129 6.1 邊緣輕量化的簡介·······································································129 6.1.1 邊緣端對輕量化的需求························································129 6.1.2 什么是邊緣輕量化······························································130 6.2 模型壓縮方法·············································································131 6.2.1 量化和二值化····································································131 6.2.2 網(wǎng)絡剪枝··········································································131 6.2.3 低秩因子分解····································································132 6.2.4 參數(shù)共享··········································································133 6.2.5 蒸餾學習··········································································133 6.2.6 加速網(wǎng)絡設計····································································134 6.3 模型壓縮舉例·············································································137 6.3.1 知識蒸餾··········································································137 6.3.2 深度壓縮··········································································139 6.3.3 MNASNet ·········································································143 本章習題··························································································145 第 7 章 云端決策 ···················································································146 7.1 云端決策簡介·············································································147 7.1.1 云端決策的重要性······························································147 7.1.2 云端決策的特點·································································147 7.2 云端決策——大數(shù)據(jù)挖掘······························································149 7.2.1 回歸分析··········································································149 7.2.2 聚類 ················································································150 7.2.3 關聯(lián)規(guī)則··········································································152 7.3 云端決策——推薦算法·································································154 7.3.1 基于統(tǒng)計的推薦算法···························································155 7.3.2 基于協(xié)同過濾的推薦系統(tǒng)·····················································155 7.3.3 基于內(nèi)容的推薦系統(tǒng)···························································156 7.3.4 基于關聯(lián)規(guī)則的推薦系統(tǒng)·····················································158 7.3.5 基于網(wǎng)絡結構的推薦系統(tǒng)·····················································158 本章習題··························································································159 第 8 章 邊云智能賦能智慧教室 ·································································160 8.1 智慧教室的形成背景與邊云框架·····················································161 8.1.1 智慧教室政策支持與特征分析 ··············································162 8.1.2 基于邊云智能的智慧教室框架 ··············································164 8.1.3 基于邊云智能建設的智慧教室目標愿景 ··································166 8.2 智慧教室的邊緣端感知技術與應用··················································166 8.2.1 無感考勤、表情感知與異常行為識別 ·····································167 8.2.2 邊緣端感知模型的壓縮與輕量化 ···········································172 8.3 智慧教室的云端決策技術與應用·····················································174 8.3.1 “教育大腦”大數(shù)據(jù)分析決策方法··········································174 8.3.2 個性化推薦、學習評價與師生互動應用 ··································176 8.4 “邊云智能+”前景展望·································································178 8.4.1 邊云智能賦能智慧交通························································178 8.4.2 邊云智能賦能智慧安防························································185 本章習題··························································································190 習題答案································································································191
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