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邊云智能數(shù)據(jù)分析與應(yīng)用

邊云智能數(shù)據(jù)分析與應(yīng)用

作者：沈鈞戈等
出版社：電子工業(yè)出版社出版時(shí)間：2023-08-01
開本： 其他 頁數(shù)： 216
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邊云智能數(shù)據(jù)分析與應(yīng)用 內(nèi)容簡介

隨著“十四五”規(guī)劃綱要中提出“協(xié)同發(fā)展云服務(wù)與邊緣計(jì)算服務(wù)”的觀點(diǎn)，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(chǔ)知識(shí)和應(yīng)用。書中分為四個(gè)篇章，**篇章介紹了邊云架構(gòu)的骨架和基礎(chǔ)概念，第二篇章介紹了人工智能算法和深度學(xué)習(xí)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(lǐng)域的應(yīng)用。本書可以使讀者了解邊云計(jì)算的基本概念和原理邏輯，熟悉基本的人工智能計(jì)算方法和數(shù)據(jù)分析的邏輯和運(yùn)用場景。通過數(shù)據(jù)科學(xué)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(yīng)用提升，并培養(yǎng)數(shù)據(jù)導(dǎo)向思維方式，為未來學(xué)習(xí)智能無人系統(tǒng)科學(xué)與技術(shù)學(xué)科打下基礎(chǔ)。 本書目標(biāo)明確，技術(shù)優(yōu)選，強(qiáng)調(diào)課程思政和潤物無聲的教育理念，旨在提高學(xué)生的數(shù)據(jù)科學(xué)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(xué)與技術(shù)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(jì)算等學(xué)科，具有交叉性的特點(diǎn)。同時(shí)，資深從業(yè)者也可將其作為參考書籍。

邊云智能數(shù)據(jù)分析與應(yīng)用 目錄

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(shù)的快速發(fā)展·····················································2
1.1.2 國家政策的支持與引導(dǎo)···························································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(shù)新融合···························································.13
1.4.2 多維度場景應(yīng)用·································································.14
本章習(xí)題··························································································.15
第 2 章 邊云架構(gòu) ···················································································.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(gòu)模型·································································.20
2.2.1 概念框架··········································································.20
2.2.2 層次結(jié)構(gòu)··········································································.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(biāo)··········································································.28
2.4 邊云智能架構(gòu)應(yīng)用·······································································.30
2.4.1 “云-邊-端”區(qū)塊鏈 ·····························································.30
2.4.2 “云-邊-端”一體化機(jī)器人系統(tǒng) ··············································.32
本章習(xí)題··························································································.33
第 3 章 深度學(xué)習(xí) ···················································································.35
3.1 深度學(xué)習(xí)概念·············································································.36
3.1.1 人工智能與機(jī)器學(xué)習(xí)···························································.36
3.1.2 深度學(xué)習(xí)··········································································.37
3.1.3 神經(jīng)網(wǎng)絡(luò)··········································································.39
3.2 前饋神經(jīng)網(wǎng)絡(luò)·············································································.39
3.2.1 感知機(jī)模型·······································································.39
3.2.2 反向傳播··········································································.42
3.2.3 卷積神經(jīng)網(wǎng)絡(luò)····································································.44
3.2.4 幾種典型的卷積神經(jīng)網(wǎng)絡(luò)·····················································.47
3.3 反饋神經(jīng)網(wǎng)絡(luò)·············································································.50
3.3.1 循環(huán)神經(jīng)網(wǎng)絡(luò)····································································.50
3.3.2 長短期神經(jīng)網(wǎng)絡(luò)·································································.53
3.4 Transformer 神經(jīng)網(wǎng)絡(luò) ···································································.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(jī)制·································································.59
3.4.3 非參位置編碼····································································.60
本章習(xí)題··························································································.61
第 4 章 自然語言處理 ··············
展開全部

邊云智能數(shù)據(jù)分析與應(yīng)用 作者簡介

沈鈞戈，西北工業(yè)大學(xué)助理教授，陜西省電子學(xué)會(huì)圖形圖象專委會(huì)委員，主講課程為智慧城市與計(jì)算機(jī)視覺，并負(fù)責(zé)相關(guān)慕課建設(shè)。

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