第 1 章 緒論·····················································································1
1.1 人工智能的內(nèi)涵及發(fā)展····································································1
1.2 工業(yè)人工智能的內(nèi)涵及發(fā)展······························································4
1.2.1 工業(yè)人工智能的發(fā)展背景 ·····························································4
1.2.2 工業(yè)人工智能的發(fā)展歷程 ·····························································6
1.2.3 工業(yè)人工智能與傳統(tǒng)人工智能的區(qū)別················································7
1.3 工業(yè)人工智能的算法功能和典型應(yīng)用··················································8
1.4 工業(yè)人工智能的知識體系································································11
1.5 工業(yè)人工智能的關(guān)鍵技術(shù)································································12
第 2 章 數(shù)據(jù)分析與數(shù)據(jù)挖掘工具··························································14
2.1 工業(yè)大數(shù)據(jù)的特點(diǎn)··········································································15
2.2 工業(yè)大數(shù)據(jù)挖掘的定義及過程···························································17
2.3 工業(yè)數(shù)據(jù)的特征分析·······································································18
2.3.1 工業(yè)數(shù)據(jù)的統(tǒng)計(jì)描述 ···································································18
2.3.2 工業(yè)數(shù)據(jù)的相關(guān)性分析 ································································20
2.4 工業(yè)數(shù)據(jù)的預(yù)處理··········································································23
2.4.1 異常值處理 ···············································································23
工業(yè)數(shù)據(jù)異常值的處理案例 ·····························································25
2.4.2 缺失值處理 ···············································································26
工業(yè)數(shù)據(jù)缺失值的處理案例 ·····························································27
2.4.3 數(shù)據(jù)歸一化處理 ·········································································28
2.4.4 不等時(shí)距數(shù)據(jù)處理 ······································································28
工業(yè)數(shù)據(jù)不等時(shí)距處理案例·····························································.29
2.4.5 離群點(diǎn)檢測···············································································.32
2.4.6 不平衡數(shù)據(jù)集檢測······································································.34
2.4.7 數(shù)據(jù)降維處理············································································.36
2.5 假設(shè)檢驗(yàn)····················································································.41
2.5.1 假設(shè)檢驗(yàn)的基本原理···································································.41
2.5.2 假設(shè)檢驗(yàn)的一般步驟···································································.42
2.5.3 假設(shè)檢驗(yàn)的種類·········································································.42
2.6 多變量分析模型 ··········································································.44
2.6.1 線性模型··················································································.44
2.6.2 廣義線性模型············································································.45
2.6.3 線性混合效應(yīng)模型······································································.47
2.7 最大似然估計(jì)··············································································.48
2.8 關(guān)聯(lián)分析····················································································.49
2.8.1 關(guān)聯(lián)規(guī)則挖掘···········································································.·51
2.8.2 關(guān)聯(lián)規(guī)則算法···········································································.·52
2.8.3 時(shí)序關(guān)聯(lián)規(guī)則算法······································································.54
2.8.4 一維時(shí)序關(guān)聯(lián)規(guī)則······································································.56
2.8.5 多維時(shí)序關(guān)聯(lián)規(guī)則······················································.··············.59
2.9 數(shù)據(jù)分析系統(tǒng)的體系架構(gòu) ··············································.··············.64
第 3 章 機(jī)器學(xué)習(xí) ····························································.···············.65
3.1 人類的思考過程與機(jī)器學(xué)習(xí)的過程 ···················································.65
3.2 機(jī)器學(xué)習(xí)的知識體系 ···················································.················.67
3.3 Python 語言的優(yōu)勢·········································································.68
3.4 工業(yè)人工智能的有監(jiān)督學(xué)習(xí)算法 ·······················································.68
3.4.1 決策樹算法···············································································.··70
決策樹算法的應(yīng)用案例：判斷影響打網(wǎng)球的相關(guān)因素····························73
3.4.2 回歸算法·····················································································75
3.4.3 支持向量機(jī)算法 ··········································································80
有監(jiān)督學(xué)習(xí)算法的工業(yè)應(yīng)用案例······························································86
3.5 工業(yè)人工智能的無監(jiān)督學(xué)習(xí)算法·························································87
3.5.1 聚類算法 ···················································································87
3.5.2 自動編碼器 ················································································94
無監(jiān)督學(xué)習(xí)算法的工業(yè)應(yīng)用案例·······························································95
3.6 機(jī)器學(xué)習(xí)的主要挑戰(zhàn)及應(yīng)對方法··························································97
3.6.1 機(jī)器學(xué)習(xí)的主要挑戰(zhàn) ·····································································97
3.6.2 應(yīng)對挑戰(zhàn)的主要方法 ····································································100
第 4 章 深度學(xué)習(xí)········································································.·······103
4.1 深度學(xué)習(xí)的核心·····································································.········103
4.2 深度學(xué)習(xí)的常用網(wǎng)絡(luò)：卷積神經(jīng)網(wǎng)絡(luò)····················································106
4.2.1 卷積神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu) ·····································································107
4.2.2 卷積神經(jīng)網(wǎng)絡(luò)的關(guān)鍵技術(shù) ·······························································109
4.2.3 卷積神經(jīng)網(wǎng)絡(luò)的訓(xùn)練 ·····································································112
卷積神經(jīng)網(wǎng)絡(luò)的工業(yè)應(yīng)用案例························.·································113
4.3 深度學(xué)習(xí)的常用網(wǎng)絡(luò)：循環(huán)神經(jīng)網(wǎng)絡(luò)··················.································115
4.3.1 循環(huán)神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu) ····································. ·······························115
4.3.2 循環(huán)神經(jīng)網(wǎng)絡(luò)的優(yōu)化 ·····································································117
循環(huán)神經(jīng)網(wǎng)絡(luò)的工業(yè)應(yīng)用案例··························································120
4.4 深度學(xué)習(xí)的常用網(wǎng)絡(luò)：受限玻爾茲曼機(jī)·················································126
4.4.1 受限玻爾茲曼機(jī)的分類 ··································································126
4.4.2 受限玻爾茲曼機(jī)的訓(xùn)練 ··································································131
4.5 深度學(xué)習(xí)的常用網(wǎng)絡(luò)：深度置信網(wǎng)絡(luò)····················································134
4.5.1 深度置信網(wǎng)絡(luò)的構(gòu)建 ·····································································134
4.5.2 深度置信網(wǎng)絡(luò)的訓(xùn)練 ·····································································135
深度置信網(wǎng)絡(luò)的工業(yè)應(yīng)用案例··························································137
4.6 深度學(xué)習(xí)的常用框架·········································································143
4.6.1 Theano·····················································································.143
4.6.2 TensorFlow··············································································. 144
4.6.3 Keras ·······················································································.146
4.6.4 Caffe/Caffe2···········································································. ·146
4.6.5 MXNet ····················································································. 147
4.6.6 CNTK······················································································. 148
第 5 章 工業(yè)人工智能面臨的挑戰(zhàn) ···························································149
5.1 工業(yè)人工智能帶來的變革 ·································································.149
5.2 工業(yè)人工智能面臨的挑戰(zhàn) ·································································.150
參考文獻(xiàn)·······························································································153