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深度學(xué)習(xí)與神經(jīng)網(wǎng)絡(luò) 讀者對象:本書可作為高等院校人工智能、電子信息、計(jì)算機(jī)等專業(yè)的研究生或本科生教材,也可 用作相關(guān)領(lǐng)域的研究和工程技術(shù)人員的參考書籍。
全書分為7 個(gè)章節(jié)。第1 章緒論,梳理了人工智能不同技術(shù)流派的特點(diǎn)、深度學(xué)習(xí)的發(fā)展及前沿技術(shù);第2 章介紹相關(guān)預(yù)備知識(shí),包括線性代數(shù)、概率論、優(yōu)化理論以及機(jī)器學(xué)習(xí)的基礎(chǔ)知識(shí);第3 章從前饋神經(jīng)網(wǎng)絡(luò)的基礎(chǔ)模型——感知器出發(fā),介紹前饋神經(jīng)網(wǎng)絡(luò)的基本結(jié)構(gòu)以及涉及的激活函數(shù)、梯度下降、反向傳播等內(nèi)容;第4 章,介紹深度模型的優(yōu)化問題,討論了神經(jīng)網(wǎng)絡(luò)優(yōu)化中常見的病態(tài)問題;第5 章介紹深度學(xué)習(xí)中的正則化方法,包括范數(shù)懲罰、數(shù)據(jù)集增強(qiáng)與噪聲注入、提前停止等;第6 章介紹了卷積神經(jīng)網(wǎng)絡(luò),以及卷積神經(jīng)網(wǎng)絡(luò)在計(jì)算機(jī)視覺領(lǐng)域的具體應(yīng)用;第7 章通過實(shí)際案例介紹循環(huán)神經(jīng)網(wǎng)絡(luò)與卷積神經(jīng)網(wǎng)絡(luò)的結(jié)合應(yīng)用。
趙金晶,女,1981年生,軍事科學(xué)院系統(tǒng)工程研究院高級工程師,國防科技大學(xué)計(jì)算機(jī)學(xué)院博士畢業(yè),主要研究方向?yàn)榫W(wǎng)絡(luò)與信息安全、人工智能技術(shù)。先后承擔(dān)國家自然科學(xué)基金、國家973計(jì)劃、863計(jì)劃等重大項(xiàng)目,曾獲國家自然科學(xué)基金青年基金資助。獲省部級科技進(jìn)步獎(jiǎng)二等獎(jiǎng)5項(xiàng)、三等獎(jiǎng)1項(xiàng),發(fā)明專利20余項(xiàng)。發(fā)表學(xué)術(shù)論文80余篇,出版學(xué)術(shù)著作2部。電子郵箱:zhjj0420@126.com李虎,男,1987年生,軍事科學(xué)院系統(tǒng)工程研究院工程師,國防科技大學(xué)計(jì)算機(jī)學(xué)院博士畢業(yè),主要研究方向?yàn)榫W(wǎng)絡(luò)與信息安全。先后承擔(dān)、參與國家973計(jì)劃、863計(jì)劃、裝備預(yù)研等各類科研項(xiàng)目10余項(xiàng)。獲省部級科技進(jìn)步獎(jiǎng)二等獎(jiǎng)2項(xiàng)、發(fā)明專利10余項(xiàng),發(fā)表論文20余篇。電子郵箱:lihu@nudt.edu.cn張明,男,1990年生,軍事科學(xué)院系統(tǒng)工程研究院工程師,北京系統(tǒng)工程研究所碩士畢業(yè),主要研究方向?yàn)闄C(jī)器學(xué)習(xí)和人工智能安全。先后參與、主持國家自然科學(xué)基金、重點(diǎn)實(shí)驗(yàn)室基金、國家973重大項(xiàng)目、裝備預(yù)研和國防科技創(chuàng)新特區(qū)等各類科研項(xiàng)目10余項(xiàng)。獲省部級科技進(jìn)步獎(jiǎng)一等獎(jiǎng)1項(xiàng),二等獎(jiǎng)2項(xiàng)。發(fā)表學(xué)術(shù)論文20余篇,其中SCI檢索5篇,EI檢索10余篇。電子郵箱:zm_stiss@163.com
第1 章 緒論····················································································.1
1.1 人工智能·············································································.2 1.1.1 人工智能技術(shù)的發(fā)展歷程···············································.3 1.1.2 人工智能技術(shù)的流派·····················································.9 1.2 深度學(xué)習(xí)與神經(jīng)網(wǎng)絡(luò)概述······················································.11 1.2.1 深度學(xué)習(xí)與神經(jīng)網(wǎng)絡(luò)技術(shù)的發(fā)展歷程······························.11 1.2.2 深度學(xué)習(xí)與神經(jīng)網(wǎng)絡(luò)的前沿技術(shù)····································.16 1.3 深度學(xué)習(xí)系統(tǒng)架構(gòu)·······························································.17 1.4 深度學(xué)習(xí)框架·····································································.19 1.5 深度學(xué)習(xí)的應(yīng)用··································································.20 1.5.1 計(jì)算機(jī)視覺·······························································.20 1.5.2 語音語義··································································.21 1.5.3 自然語言處理····························································.22 1.6 人工智能潛在的安全風(fēng)險(xiǎn)······················································.22 1.6.1 數(shù)據(jù)層面的風(fēng)險(xiǎn)·························································.23 1.6.2 算法模型層面的風(fēng)險(xiǎn)···················································.23 1.6.3 智能計(jì)算框架層面的風(fēng)險(xiǎn)·············································.23 1.6.4 基礎(chǔ)軟硬件層面的風(fēng)險(xiǎn)················································.24 1.6.5 應(yīng)用服務(wù)層面的風(fēng)險(xiǎn)···················································.24 本章小結(jié)··················································································.24 第2 章 預(yù)備知識(shí)············································································.25 2.1 相關(guān)數(shù)學(xué)基礎(chǔ)·····································································.25 2.1.1 線性代數(shù)··································································.25 2.1.2 概率論·····································································.27 2.1.3 優(yōu)化理論··································································.32 2.2 機(jī)器學(xué)習(xí)基礎(chǔ)·····································································.34 2.2.1 機(jī)器學(xué)習(xí)算法的基本流程·············································.35 2.2.2 機(jī)器學(xué)習(xí)常用評價(jià)指標(biāo)················································.36 2.2.3 典型機(jī)器學(xué)習(xí)算法······················································.41 2.3 實(shí)驗(yàn)環(huán)境基礎(chǔ)·····································································.49 2.3.1 GPU 驅(qū)動(dòng)的安裝配置··················································.49 2.3.2 依賴環(huán)境的安裝配置···················································.52 2.3.3 深度學(xué)習(xí)框架的安裝配置·············································.54 2.3.4 集成開發(fā)環(huán)境的安裝配置·············································.56 本章小結(jié)··················································································.57 第3 章 前饋神經(jīng)網(wǎng)絡(luò)······································································.58 3.1 感知器··············································································.58 3.1.1 單層感知器·······························································.58 3.1.2 多層感知器·······························································.61 3.1.3 前饋神經(jīng)網(wǎng)絡(luò)的基本結(jié)構(gòu)·············································.62 3.2 激活函數(shù)···········································································.63 3.2.1 Sigmoid 函數(shù)·····························································.63 3.2.2 ReLU 函數(shù)································································.65 3.2.3 Tanh 函數(shù)·································································.67 3.2.4 Softmax 函數(shù)·····························································.68 3.3 誤差反向傳播·····································································.69 3.3.1 梯度下降法·······························································.69 3.3.2 鏈?zhǔn)椒▌t··································································.70 3.3.3 反向傳播··································································.72 本章小結(jié)··················································································.75 第4 章 深度模型的優(yōu)化···································································.76 4.1 神經(jīng)網(wǎng)絡(luò)的優(yōu)化問題····························································.76 4.1.1 局部最優(yōu)和振蕩陷阱···················································.76 4.1.2 梯度爆炸和梯度消失···················································.78 4.2 常見的優(yōu)化算法··································································.80 4.2.1 梯度下降優(yōu)化算法······················································.80 4.2.2 二階優(yōu)化算法····························································.81 4.3 自適應(yīng)學(xué)習(xí)率算法·······························································.85 4.3.1 AdaGrad 算法····························································.85 4.3.2 RMSprop 算法···························································.86 4.3.3 Adam 算法································································.86 4.4 參數(shù)初始化方法··································································.87 4.4.1 隨機(jī)初始化·······························································.87 4.4.2 Xavier 初始化····························································.88 4.4.3 He 初始化·································································.89 本章小結(jié)··················································································.90 第5 章 深度學(xué)習(xí)中的正則化·····························································.91 5.1 范數(shù)懲罰···········································································.91 5.1.1 L1 正則化··································································.91 5.1.2 L2 正則化··································································.93 5.2 數(shù)據(jù)集增強(qiáng)與噪聲注入·························································.94 5.2.1 數(shù)據(jù)集增強(qiáng)·······························································.94 5.2.2 噪聲注入··································································.95 5.3 提前停止···········································································.97 5.4 Dropout ·············································································.97 5.5 批歸一化···········································································.99 本章小結(jié)·················································································.102 第6 章 卷積神經(jīng)網(wǎng)絡(luò)······································································103 6.1 卷積神經(jīng)網(wǎng)絡(luò)的發(fā)展歷程·····················································.103 6.2 卷積神經(jīng)網(wǎng)絡(luò)的基本組成·····················································.104 6.2.1 卷積層····································································.105 6.2.2 池化層····································································.106 6.2.3 全連接層·································································.108 6.3 常見卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)························································.108 6.3.1 VGG 網(wǎng)絡(luò)································································.109 6.3.2 GoogLeNet 網(wǎng)絡(luò)························································.111 6.3.3 ResNet 網(wǎng)絡(luò)·····························································.117 6.4 深度生成網(wǎng)絡(luò)····································································.122 6.4.1 生成對抗網(wǎng)絡(luò)···························································.122 6.4.2 深度卷積生成對抗網(wǎng)絡(luò)···············································.123 6.5 圖像分類案例····································································.134 6.5.1 步驟1:搭建環(huán)境······················································.135 6.5.2 步驟2:導(dǎo)入依賴庫···················································.137 6.5.3 步驟3:獲取數(shù)據(jù)······················································.137 6.5.4 步驟4:定義AlexNet 網(wǎng)絡(luò)··········································.138 6.5.5 步驟5:模型初始化···················································.140 6.5.6 步驟6:模型訓(xùn)練······················································.140 6.6 目標(biāo)檢測案例····································································.143 6.6.1 步驟1:環(huán)境配置和模型下載·······································.144 6.6.2 步驟2:主函數(shù)解析···················································.151 6.6.3 步驟3:終端指令運(yùn)行················································.152 本章小結(jié)·················································································.153 第7 章 循環(huán)神經(jīng)網(wǎng)絡(luò)·····································································.154 7.1 循環(huán)神經(jīng)網(wǎng)絡(luò)的基本原理·····················································.154 7.1.1 循環(huán)神經(jīng)網(wǎng)絡(luò)的原理··················································.154 7.1.2 雙向循環(huán)神經(jīng)網(wǎng)絡(luò)·····················································.157 7.2 循環(huán)神經(jīng)網(wǎng)絡(luò)在實(shí)際中的應(yīng)用···············································.158 7.2.1 文本生成·································································.159 7.2.2 語音識(shí)別·································································.159 7.2.3 機(jī)器翻譯·································································.160 7.2.4 生成圖像描述···························································.161 7.2.5 視頻動(dòng)作檢測···························································.162 7.2.6 信號分類·································································.162 7.3 長短期記憶網(wǎng)絡(luò)及其他門控循環(huán)神經(jīng)網(wǎng)絡(luò)································.163 7.3.1 長短期記憶網(wǎng)絡(luò)························································.164 7.3.2 其他門控循環(huán)神經(jīng)網(wǎng)絡(luò)···············································.166 7.4 深度學(xué)習(xí)在文本和序列中的應(yīng)用············································.167 7.4.1 文本數(shù)據(jù)處理···························································.167 7.4.2 文本分類和情感分析··················································.180 7.4.3 機(jī)器翻譯·································································.180 7.4.4 命名實(shí)體識(shí)別···························································.182 7.5 卷積神經(jīng)網(wǎng)絡(luò)與循環(huán)神經(jīng)網(wǎng)絡(luò)···············································.183 7.5.1 卷積神經(jīng)網(wǎng)絡(luò)與循環(huán)神經(jīng)網(wǎng)絡(luò)的對比·····························.183 7.5.2 卷積神經(jīng)網(wǎng)絡(luò)與循環(huán)神經(jīng)網(wǎng)絡(luò)的組合應(yīng)用·······················.184 7.6 案例:深度學(xué)習(xí)的詩歌生成··················································.185 7.6.1 步驟1:導(dǎo)入依賴庫···················································.186 7.6.2 步驟2:讀取數(shù)據(jù)······················································.187 7.6.3 步驟3:構(gòu)造數(shù)據(jù)集···················································.188 7.6.4 步驟4:構(gòu)造模型······················································.190 7.6.5 步驟5:訓(xùn)練過程······················································.192 7.6.6 步驟6:生成文本······················································.195 7.7 案例:基于LSTM 算法的股票預(yù)測·········································.196 7.7.1 步驟1:導(dǎo)入依賴庫···················································.196 7.7.2 步驟2:獲取并處理數(shù)據(jù)·············································.197 7.7.3 步驟3:構(gòu)建預(yù)測數(shù)據(jù)序列··········································.199 7.7.4 步驟4:構(gòu)建LSTM 網(wǎng)絡(luò)············································.201 7.7.5 步驟5:訓(xùn)練網(wǎng)絡(luò)······················································.202 7.7.6 步驟6:預(yù)測測試集···················································.203 7.8 案例:基于深度學(xué)習(xí)的文本分類············································.204 7.8.1 步驟1:項(xiàng)目入口······················································.205 7.8.2 步驟2:訓(xùn)練模塊······················································.207 7.8.3 步驟3:驗(yàn)證和測試函數(shù)·············································.210 7.8.4 步驟4:數(shù)據(jù)預(yù)處理模塊·············································.211 7.8.5 步驟5:定義模型······················································.215 7.8.6 步驟6:分類結(jié)果展示················································.218 本章小結(jié)·················································································.219
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