邊雲智能數據分析與應用
沈鈞戈 等
- 出版商: 電子工業
 - 出版日期: 2023-08-01
 - 售價: $330
 - 語言: 簡體中文
 - 頁數: 216
 - ISBN: 7121460424
 - ISBN-13: 9787121460425
 - 
    相關分類:
    
      Edge computing、DeepLearning、Data-visualization
 
下單後立即進貨 (約4週~6週)
商品描述
隨著“十四五”規劃綱要中提出“協同發展雲服務與邊緣計算服務”的觀點,邊雲智能已成為未來發展的重要趨勢。本書依托於政策大背景,旨在向讀者介紹邊雲智能的基礎知識和應用。書中分為四個篇章,第一篇章介紹了邊雲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,第三篇章介紹了雲端決策算法和邊緣端輕量化算法,第四篇章介紹了邊雲智能在智慧教育領域的應用。本書可以使讀者瞭解邊雲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據分析的邏輯和運用場景。通過數據科學的思路和方法,讀者可以將無人系統的數據智能化應用提升,並培養數據導向思維方式,為未來學習智能無人系統科學與技術學科打下基礎。 本書目標明確,技術先進,強調課程思政和潤物無聲的教育理念,旨在提高學生的數據科學素養和“用數據”的能力。本書面向智能無人系統科學與技術專業的研究生,涵蓋人工智能、大數據分析、數據挖掘和邊雲計算等學科,具有交叉性的特點。同時,資深從業者也可將其作為參考書籍。
目錄大綱
第 1 章 緒論 ····························································································1 
1.1 邊雲智能產生的大背景····································································1 
1.1.1 新一代信息技術的快速發展·····················································2 
1.1.2 國家政策的支持與引導···························································6 
1.2 邊雲智能······················································································7 
1.3 邊雲智能的發展·············································································9 
1.3.1 邊雲智能的三大發展階段························································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 邊雲架構 ···················································································.16 
2.1 系統工程方法論··········································································.17 
2.1.1 概述 ················································································.17 
2.1.2 基本方法··········································································.17 
2.2 邊雲智能體系架構模型·································································.20 
2.2.1 概念框架··········································································.20 
2.2.2 層次結構··········································································.22 
2.3 協同模式···················································································.23 
2.3.1 “雲-邊”協同 ····································································.24 
2.3.2 “邊-邊”協同 ····································································.25 
2.3.3 “邊-端”協同 ····································································.27 
2.3.4 “雲-邊-端”協同 ································································.28 
2.3.5 度量指標··········································································.28 
2.4 邊雲智能架構應用·······································································.30 
2.4.1 “雲-邊-端”區塊鏈 ·····························································.30 
2.4.2 “雲-邊-端”一體化機器人系統 ··············································.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 神經網絡··········································································.39 
3.2 前饋神經網絡·············································································.39 
3.2.1 感知機模型·······································································.39 
3.2.2 反向傳播··········································································.42 
3.2.3 捲積神經網絡····································································.44 
3.2.4 幾種典型的捲積神經網絡·····················································.47 
3.3 反饋神經網絡·············································································.50 
3.3.1 循環神經網絡····································································.50 
3.3.2 長短期神經網絡·································································.53 
3.4 Transformer 神經網絡 ···································································.56 
3.4.1 編碼器單元與解碼器單元·····················································.58 
3.4.2 多頭註意力機制·································································.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 自然語言處理的發展歷史·····················································.74 
4.1.3 自然語言處理的應用及面臨的挑戰·········································.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·············································································.93 
4.4.1 自動問答系統簡介······························································.93 
4.4.2 自動問答系統模型······························································.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 電腦視覺 ················································································107 
5.1 電腦視覺概述··········································································107 
5.1.1 電腦視覺簡介·································································108 
5.1.2 電腦視覺的發展歷史························································109 
5.1.3 電腦視覺的應用及面臨的挑戰···········································.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 什麽是邊緣輕量化······························································130 
6.2 模型壓縮方法·············································································131 
6.2.1 量化和二值化····································································131 
6.2.2 網絡剪枝··········································································131 
6.2.3 低秩因子分解····································································132 
6.2.4 參數共享··········································································133 
6.2.5 蒸餾學習··········································································133 
6.2.6 加速網絡設計····································································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 雲端決策 ···················································································146 
7.1 雲端決策簡介·············································································147 
7.1.1 雲端決策的重要性······························································147 
7.1.2 雲端決策的特點·································································147 
7.2 雲端決策——大數據挖掘······························································149 
7.2.1 回歸分析··········································································149 
7.2.2 聚類 ················································································150 
7.2.3 關聯規則··········································································152 
7.3 雲端決策——推薦算法·································································154
7.3.1 基於統計的推薦算法···························································155 
7.3.2 基於協同過濾的推薦系統·····················································155 
7.3.3 基於內容的推薦系統···························································156 
7.3.4 基於關聯規則的推薦系統·····················································158 
7.3.5 基於網絡結構的推薦系統·····················································158 
本章習題··························································································159 
第 8 章 邊雲智能賦能智慧教室 ·································································160 
8.1 智慧教室的形成背景與邊雲框架·····················································161 
8.1.1 智慧教室政策支持與特徵分析 ··············································162 
8.1.2 基於邊雲智能的智慧教室框架 ··············································164 
8.1.3 基於邊雲智能建設的智慧教室目標願景 ··································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 智慧教室的雲端決策技術與應用·····················································174 
8.3.1 “教育大腦”大數據分析決策方法··········································174 
8.3.2 個性化推薦、學習評價與師生互動應用 ··································176 
8.4 “邊雲智能+”前景展望·································································178 
8.4.1 邊雲智能賦能智慧交通························································178 
8.4.2 邊雲智能賦能智慧安防························································185 
本章習題··························································································190 
習題答案································································································191
