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解析深度學習(第2版) 版權信息
- ISBN：9787121491665
- 條形碼：9787121491665 ; 978-7-121-49166-5
- 裝幀：平裝-膠訂
- 冊數：暫無
- 重量：暫無
- 所屬分類：
解析深度學習(第2版) 內容簡介
深度學習是一種以人工神經網絡等為架構，對數據資料進行表示學習的算法，它是計算機科學及人工智能的重要分支，其代表性成果如卷積神經網絡、循環神經網絡等作為信息產業與工業互聯網等行業的主流工具性技術已被成功應用于諸多現實場景.本書作為一本面向中文讀者的深度學習教科書，從“理論與實踐相結合”的角度立意，貫徹“知行合一”的教育理念. 全書除緒論和附錄外，共有15 章，分四篇：**篇“機器學習”（第1 章），介紹機器學習的基本術語、基礎理論與模型；第二篇“深度學習基礎”（第2 ～ 5 章），介紹深度學習基本概念、卷積神經網絡、循環神經網絡和Transformer 網絡等內容；第三篇“深度學習實踐”（第6 ～ 14 章），介紹深度學習模型自數據準備開始，到網絡參數初始化、不同網絡部件的選擇、網絡配置、網絡模型訓練、不平衡樣本的處理，*終到模型集成等實踐應用技巧和經驗；第四篇“深度學習進階”（第15 章），本篇以計算機視覺中的基礎任務為例，介紹深度學習的進階發展及其應用情況.本書不是一本編程類圖書，而是希望通過“基礎知識”和“實踐技巧”兩方面的內容使讀者從更高維度了解、掌握并成功構建針對自身應用的深度學習模型.本書可作為高等院校計算機、人工智能、自動化及相關專業的本科生或研究生教材，也可供對深度學習感興趣的研究人員和工程技術人員閱讀參考.
解析深度學習(第2版) 目錄
第0 章緒論·············1
0.1 引子·············...1
0.2 人工智能············..2
0.3 深度學習············..5
參考文獻··············9
**篇機器學習···········...11
第1 章機器學習基礎··········13
1.1 機器學習基本術語··········.13
1.2 模型評估與選擇··········...14
1.2.1 經驗誤差與過擬合·········15
1.2.2 常用的評估方法·········..16
1.2.3 性能度量···········.17
1.2.4 偏差與方差··········...20
1.3 線性模型············..21
1.3.1 基本形式···········.21
1.3.2 線性回歸···········.22
1.3.3 線性判別分析··········23
1.3.4 多分類學習··········...25
1.3.5 支持向量機··········...26
1.4 機器學習基本理論··········.29
1.4.1 PAC 學習理論··········30
1.4.2 No Free Lunch 定理········..31
1.4.3 奧卡姆剃刀原理·········..32
1.4.4 歸納偏置···········.32
1.5 總結與擴展閱讀··········...33
目錄ix
1.6 習題·············...34
參考文獻··············35
第二篇深度學習基礎··········.37
第2 章深度學習基本概念········...39
2.1 發展歷程············..39
2.2 “端到端”思想··········...43
2.3 基本結構············..45
2.4 前饋運算············..47
2.5 反饋運算············..47
2.6 總結與擴展閱讀··········...50
2.7 習題·············...51
參考文獻··············52
第3 章卷積神經網絡··········54
3.1 卷積神經網絡基本組件與操作·······.54
3.1.1 符號表示···········.55
3.1.2 卷積層···········...55
3.1.3 匯合層···········...59
3.1.4 激活函數···········.61
3.1.5 全連接層···········.63
3.1.6 目標函數···········.63
3.2 卷積神經網絡經典結構·········64
3.2.1 卷積神經網絡結構中的重要概念·····...64
3.2.2 經典網絡案例分析·········68
3.3 卷積神經網絡的壓縮·········..81
3.3.1 低秩近似···········.82
3.3.2 剪枝與稀疏約束·········..84
3.3.3 參數量化···········.88
3.3.4 二值網絡···········.91
3.3.5 知識蒸餾···········.93
x 目錄
3.3.6 緊湊的網絡結構·········..95
3.4 總結與擴展閱讀··········...97
3.5 習題·············...99
參考文獻··············101
第4 章循環神經網絡··········106
4.1 循環神經網絡基本組件與操作·······.106
4.1.1 符號表示···········.107
4.1.2 記憶模塊···········.108
4.1.3 參數學習···········.110
4.1.4 長程依賴問題··········113
4.2 循環神經網絡經典結構·········115
4.2.1 長短時記憶網絡·········..115
4.2.2 門控循環單元網絡·········117
4.2.3 堆疊循環神經網絡·········118
4.2.4 雙向循環神經網絡·········119
4.3 循環神經網絡拓展··········.120
4.3.1 遞歸神經網絡··········121
4.3.2 圖神經網絡··········...122
4.4 循環神經網絡訓練··········.123
4.5 總結與擴展閱讀··········...124
4.6 習題·············...126
參考文獻··············128
第5 章Transformer 網絡········.130
5.1 Transformer 網絡基本組件與操作······.130
5.1.1 符號表示···········.131
5.1.2 位置編碼···········.132
5.1.3 多頭注意力機制·········..133
5.1.4 編碼器···········...137
5.1.5 解碼器···········...138
目錄xi
5.2 Transformer 網絡經典結構········141
5.2.1 Transformer-XL········...141
5.2.2 Longformer··········...145
5.2.3 Reformer···········.146
5.2.4 Universal Transformer·······.149
5.3 Transformer 網絡訓練·········.151
5.4 總結與擴展閱讀··········...154
5.5 習題·············...156
參考文獻··············157
第三篇深度學習實踐··········.159
第6 章數據擴充與數據預處理·······.161
6.1 簡單的數據擴充方式·········..161
6.2 特殊的數據擴充方式·········..162
6.2.1 Fancy PCA·········...162
6.2.2 監督式數據擴充·········..163
6.2.3 mixup 法···········.164
6.2.4 自動化數據擴充·········..169
6.3 深度學習數據預處理·········..171
6.4 總結與擴展閱讀··········...173
6.5 習題·············...174
參考文獻··············177
第7 章網絡參數初始化·········..179
7.1 全零初始化············179
7.2 隨機初始化············181
7.3 其他初始化方法··········...194
7.4 總結與擴展閱讀··········...194
7.5 習題·············...195
參考文獻··············197
xii 目錄
第8 章激活函數···········..198
8.1 Sigmoid 函數···········..198
8.2 tanh(x) 函數···········..199
8.3 修正線性單元（ReLU）········...200
8.4 Leaky ReLU···········201
8.5 參數化ReLU··········...201
8.6 隨機化ReLU··········...203
8.7 指數化線性單元（ELU）········..204
8.8 激活函數實踐···········..204
8.9 總結與擴展閱讀··········...206
8.10 習題·············..207
參考文獻··············209
第9 章目標函數···········..210
9.1 分類任務的目標函數·········..210
9.1.1 交叉熵損失函數·········..210
9.1.2 合頁損失函數··········216
9.1.3 坡道損失函數··········219
9.1.4 大間隔交叉熵損失函數········221
9.1.5 中心損失函數··········228
9.2 回歸任務的目標函數·········..231
9.2.1 ?1 損失函數··········..232
9.2.2 ?2 損失函數··········..235
9.2.3 Tukey’s biweight 損失函數·······238
9.3 其他任務的目標函數·········..239
9.4 總結與擴展閱讀··········...241
9.5 習題·············...242
參考文獻··············244
第10 章網絡正則化··········..245
10.1 ?2 正則化············.246
目錄xiii
10.2 ?1 正則化············.246
10.3 *大范數約束···········247
10.4 隨機失活············.247
10.5 驗證集的使用···········249
10.6 總結與擴展閱讀··········..250
10.7 習題·············..251
參考文獻··············253
第11 章超參數設定和網絡訓練······...254
11.1 網絡超參數設定··········..254
11.1.1 輸入圖像像素大小········...254
11.1.2 卷積層超參數的設定········.255
11.1.3 匯合層超參數的設定········.256
11.2 訓練技巧············.256
11.2.1 訓練數據隨機打亂········...256
11.2.2 學習率的設定·········...256
11.2.3 批規范化操作·········...258
11.2.4 網絡模型優化算法選擇·······...271
11.2.5 微調神經網絡·········...282
11.3 總結與擴展閱讀··········..286
11.4 習題·············..288
參考文獻··············289
第12 章不平衡樣本的處理········.291
12.1 數據層面處理方法··········292
12.1.1 數據重采樣··········..292
12.1.2 類別平衡采樣·········...292
12.2 算法層面處理方法··········294
12.2.1 代價敏感方法·········...294
12.2.2 代價敏感法中權重的指定方式······295
12.3 總結與擴展閱讀··········..297
12.4 習題···x
0.1 引子·············...1
0.2 人工智能············..2
0.3 深度學習············..5
參考文獻··············9
**篇機器學習···········...11
第1 章機器學習基礎··········13
1.1 機器學習基本術語··········.13
1.2 模型評估與選擇··········...14
1.2.1 經驗誤差與過擬合·········15
1.2.2 常用的評估方法·········..16
1.2.3 性能度量···········.17
1.2.4 偏差與方差··········...20
1.3 線性模型············..21
1.3.1 基本形式···········.21
1.3.2 線性回歸···········.22
1.3.3 線性判別分析··········23
1.3.4 多分類學習··········...25
1.3.5 支持向量機··········...26
1.4 機器學習基本理論··········.29
1.4.1 PAC 學習理論··········30
1.4.2 No Free Lunch 定理········..31
1.4.3 奧卡姆剃刀原理·········..32
1.4.4 歸納偏置···········.32
1.5 總結與擴展閱讀··········...33
目錄ix
1.6 習題·············...34
參考文獻··············35
第二篇深度學習基礎··········.37
第2 章深度學習基本概念········...39
2.1 發展歷程············..39
2.2 “端到端”思想··········...43
2.3 基本結構············..45
2.4 前饋運算············..47
2.5 反饋運算············..47
2.6 總結與擴展閱讀··········...50
2.7 習題·············...51
參考文獻··············52
第3 章卷積神經網絡··········54
3.1 卷積神經網絡基本組件與操作·······.54
3.1.1 符號表示···········.55
3.1.2 卷積層···········...55
3.1.3 匯合層···········...59
3.1.4 激活函數···········.61
3.1.5 全連接層···········.63
3.1.6 目標函數···········.63
3.2 卷積神經網絡經典結構·········64
3.2.1 卷積神經網絡結構中的重要概念·····...64
3.2.2 經典網絡案例分析·········68
3.3 卷積神經網絡的壓縮·········..81
3.3.1 低秩近似···········.82
3.3.2 剪枝與稀疏約束·········..84
3.3.3 參數量化···········.88
3.3.4 二值網絡···········.91
3.3.5 知識蒸餾···········.93
x 目錄
3.3.6 緊湊的網絡結構·········..95
3.4 總結與擴展閱讀··········...97
3.5 習題·············...99
參考文獻··············101
第4 章循環神經網絡··········106
4.1 循環神經網絡基本組件與操作·······.106
4.1.1 符號表示···········.107
4.1.2 記憶模塊···········.108
4.1.3 參數學習···········.110
4.1.4 長程依賴問題··········113
4.2 循環神經網絡經典結構·········115
4.2.1 長短時記憶網絡·········..115
4.2.2 門控循環單元網絡·········117
4.2.3 堆疊循環神經網絡·········118
4.2.4 雙向循環神經網絡·········119
4.3 循環神經網絡拓展··········.120
4.3.1 遞歸神經網絡··········121
4.3.2 圖神經網絡··········...122
4.4 循環神經網絡訓練··········.123
4.5 總結與擴展閱讀··········...124
4.6 習題·············...126
參考文獻··············128
第5 章Transformer 網絡········.130
5.1 Transformer 網絡基本組件與操作······.130
5.1.1 符號表示···········.131
5.1.2 位置編碼···········.132
5.1.3 多頭注意力機制·········..133
5.1.4 編碼器···········...137
5.1.5 解碼器···········...138
目錄xi
5.2 Transformer 網絡經典結構········141
5.2.1 Transformer-XL········...141
5.2.2 Longformer··········...145
5.2.3 Reformer···········.146
5.2.4 Universal Transformer·······.149
5.3 Transformer 網絡訓練·········.151
5.4 總結與擴展閱讀··········...154
5.5 習題·············...156
參考文獻··············157
第三篇深度學習實踐··········.159
第6 章數據擴充與數據預處理·······.161
6.1 簡單的數據擴充方式·········..161
6.2 特殊的數據擴充方式·········..162
6.2.1 Fancy PCA·········...162
6.2.2 監督式數據擴充·········..163
6.2.3 mixup 法···········.164
6.2.4 自動化數據擴充·········..169
6.3 深度學習數據預處理·········..171
6.4 總結與擴展閱讀··········...173
6.5 習題·············...174
參考文獻··············177
第7 章網絡參數初始化·········..179
7.1 全零初始化············179
7.2 隨機初始化············181
7.3 其他初始化方法··········...194
7.4 總結與擴展閱讀··········...194
7.5 習題·············...195
參考文獻··············197
xii 目錄
第8 章激活函數···········..198
8.1 Sigmoid 函數···········..198
8.2 tanh(x) 函數···········..199
8.3 修正線性單元（ReLU）········...200
8.4 Leaky ReLU···········201
8.5 參數化ReLU··········...201
8.6 隨機化ReLU··········...203
8.7 指數化線性單元（ELU）········..204
8.8 激活函數實踐···········..204
8.9 總結與擴展閱讀··········...206
8.10 習題·············..207
參考文獻··············209
第9 章目標函數···········..210
9.1 分類任務的目標函數·········..210
9.1.1 交叉熵損失函數·········..210
9.1.2 合頁損失函數··········216
9.1.3 坡道損失函數··········219
9.1.4 大間隔交叉熵損失函數········221
9.1.5 中心損失函數··········228
9.2 回歸任務的目標函數·········..231
9.2.1 ?1 損失函數··········..232
9.2.2 ?2 損失函數··········..235
9.2.3 Tukey’s biweight 損失函數·······238
9.3 其他任務的目標函數·········..239
9.4 總結與擴展閱讀··········...241
9.5 習題·············...242
參考文獻··············244
第10 章網絡正則化··········..245
10.1 ?2 正則化············.246
目錄xiii
10.2 ?1 正則化············.246
10.3 *大范數約束···········247
10.4 隨機失活············.247
10.5 驗證集的使用···········249
10.6 總結與擴展閱讀··········..250
10.7 習題·············..251
參考文獻··············253
第11 章超參數設定和網絡訓練······...254
11.1 網絡超參數設定··········..254
11.1.1 輸入圖像像素大小········...254
11.1.2 卷積層超參數的設定········.255
11.1.3 匯合層超參數的設定········.256
11.2 訓練技巧············.256
11.2.1 訓練數據隨機打亂········...256
11.2.2 學習率的設定·········...256
11.2.3 批規范化操作·········...258
11.2.4 網絡模型優化算法選擇·······...271
11.2.5 微調神經網絡·········...282
11.3 總結與擴展閱讀··········..286
11.4 習題·············..288
參考文獻··············289
第12 章不平衡樣本的處理········.291
12.1 數據層面處理方法··········292
12.1.1 數據重采樣··········..292
12.1.2 類別平衡采樣·········...292
12.2 算法層面處理方法··········294
12.2.1 代價敏感方法·········...294
12.2.2 代價敏感法中權重的指定方式······295
12.3 總結與擴展閱讀··········..297
12.4 習題···x
展開全部
解析深度學習(第2版) 作者簡介
東南大學計算機科學與工程學院教授、博士生導師。主要研究領域為計算機視覺和機器學習，在相關領域國際頂級期刊和會議發表論文六十余篇，Google Scholar Citations逾6000次，相關研究工作獲得首屆“江蘇省自然科學百篇優秀學術成果”以及含iNaturalist在內的計算機視覺領域國際權威賽事共8項世界冠軍。曾在CVPR等國際會議講授“細粒度圖像分析”為主題的短課程。曾入選全球前2%頂尖科學家榜單、吳文俊人工智能優秀青年獎、中國科協青年人才托舉工程、江蘇省計算機學會青年科技獎、南京經開區中青年優秀人才、《中國圖象圖形學報》優秀編委、《中國科學：信息科學》優秀評審人等榮譽，主編教材《解析深度學習（第二版）》入選省部級重點教材和“十四五”國家重點出版物出版規劃，出版學術專著1部。任IEEE Senior Member、CCF高級會員、CSIG高級會員、中國圖象圖形學報青年編委、VALSE常務AC、CCF-CV/CCF-AI等專委會執行委員。擔任ICCV、IJCAI、ACM Multimedia等國際會議Workshop程序委員會主席，ACCV 2022 Tutorial主席，CVPR、AAAI、IJCAI等會議領域主席或高級程序委員。主持國家重點研發計劃青年科學家項目等。
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