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深度學(xué)習(xí)原理與應(yīng)用實(shí)踐 版權(quán)信息
- ISBN：9787121304132
- 條形碼：9787121304132 ; 978-7-121-30413-2
- 裝幀：暫無
- 冊數(shù)：暫無
- 重量：暫無
- 所屬分類：>
深度學(xué)習(xí)原理與應(yīng)用實(shí)踐 本書特色
本書全面、系統(tǒng)地介紹深度學(xué)習(xí)相關(guān)的技術(shù)，包括人工神經(jīng)網(wǎng)絡(luò)，卷積神經(jīng)網(wǎng)絡(luò)，深度學(xué)習(xí)平臺及源代碼分析，深度學(xué)習(xí)入門與進(jìn)階，深度學(xué)習(xí)高級實(shí)踐，所有章節(jié)均附有源程序，所有實(shí)驗(yàn)讀者均可重現(xiàn)，具有高度的可操作性和實(shí)用性。通過學(xué)習(xí)本書，研究人員、深度學(xué)習(xí)愛好者，能夠在3 個(gè)月內(nèi)，系統(tǒng)掌握深度學(xué)習(xí)相關(guān)的理論和技術(shù)。
深度學(xué)習(xí)原理與應(yīng)用實(shí)踐 內(nèi)容簡介
本書全面、系統(tǒng)地介紹深度學(xué)習(xí)相關(guān)的技術(shù)，所有章節(jié)均附有源程序，所有實(shí)驗(yàn)讀者均可重現(xiàn)，具有高度的可操作性和實(shí)用性。
深度學(xué)習(xí)原理與應(yīng)用實(shí)踐 目錄
目 錄
深度學(xué)習(xí)基礎(chǔ)篇
第1 章 緒論 ·································································································· 2
1.1 引言 ······································································································· 2
1.1.1 Google 的深度學(xué)習(xí)成果 ···························································· 2
1.1.2 Microsoft 的深度學(xué)習(xí)成果························································· 3
1.1.3 國內(nèi)公司的深度學(xué)習(xí)成果 ························································· 3
1.2 深度學(xué)習(xí)技術(shù)的發(fā)展歷程 ···································································· 4
1.3 深度學(xué)習(xí)的應(yīng)用領(lǐng)域 ············································································ 6
1.3.1 圖像識別領(lǐng)域 ············································································· 6
1.3.2 語音識別領(lǐng)域 ············································································· 6
1.3.3 自然語言理解領(lǐng)域 ····································································· 7
1.4 如何開展深度學(xué)習(xí)的研究和應(yīng)用開發(fā) ················································· 7
本章參考文獻(xiàn) ······························································································ 11
第2 章 國內(nèi)外深度學(xué)習(xí)技術(shù)研發(fā)現(xiàn)狀及其產(chǎn)業(yè)化趨勢 ······························· 13
2.1 Google 在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ·················································· 13
2.1.1 深度學(xué)習(xí)在Google 的應(yīng)用 ······················································ 13
2.1.2 Google 的TensorFlow 深度學(xué)習(xí)平臺 ······································ 14
2.1.3 Google 的深度學(xué)習(xí)芯片TPU ·················································· 15
2.2 Facebook 在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ·············································· 15
2.2.1 Torchnet ···················································································· 15
2.2.2 DeepText ··················································································· 16
2.3 百度在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ······················································· 17
2.3.1 光學(xué)字符識別 ··········································································· 17
2.3.2 商品圖像搜索 ··········································································· 17
2.3.3 在線廣告 ·················································································· 18
2.3.4 以圖搜圖 ·················································································· 18
2.3.5 語音識別 ·················································································· 18
2.3.6 百度開源深度學(xué)習(xí)平臺MXNet 及其改進(jìn)的深度語音識別系統(tǒng)Warp-CTC ····· 19
2.4 阿里巴巴在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ··············································· 19
2.4.1 拍立淘 ······················································································ 19
2.4.2 阿里小蜜——智能客服Messenger ········································· 20
2.5 京東在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ······················································· 20
2.6 騰訊在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ······················································· 21
2.7 科創(chuàng)型公司（基于深度學(xué)習(xí)的人臉識別系統(tǒng)） ······························· 22
2.8 深度學(xué)習(xí)的硬件支撐——NVIDIA GPU ············································ 23
本章參考文獻(xiàn) ······························································································ 24
深度學(xué)習(xí)理論篇
第3 章 神經(jīng)網(wǎng)絡(luò) ························································································· 30
3.1 神經(jīng)元的概念 ······················································································ 30
3.2 神經(jīng)網(wǎng)絡(luò) ····························································································· 31
3.2.1 后向傳播算法 ··········································································· 32
3.2.2 后向傳播算法推導(dǎo) ··································································· 33
3.3 神經(jīng)網(wǎng)絡(luò)算法示例 ·············································································· 36
本章參考文獻(xiàn) ······························································································ 38
第4 章 卷積神經(jīng)網(wǎng)絡(luò) ················································································· 39
4.1 卷積神經(jīng)網(wǎng)絡(luò)特性 ················································································ 39
4.1.1 局部連接 ·················································································· 40
4.1.2 權(quán)值共享 ·················································································· 41
4.1.3 空間相關(guān)下采樣 ······································································· 42
4.2 卷積神經(jīng)網(wǎng)絡(luò)操作 ·················4
深度學(xué)習(xí)基礎(chǔ)篇
第1 章 緒論 ·································································································· 2
1.1 引言 ······································································································· 2
1.1.1 Google 的深度學(xué)習(xí)成果 ···························································· 2
1.1.2 Microsoft 的深度學(xué)習(xí)成果························································· 3
1.1.3 國內(nèi)公司的深度學(xué)習(xí)成果 ························································· 3
1.2 深度學(xué)習(xí)技術(shù)的發(fā)展歷程 ···································································· 4
1.3 深度學(xué)習(xí)的應(yīng)用領(lǐng)域 ············································································ 6
1.3.1 圖像識別領(lǐng)域 ············································································· 6
1.3.2 語音識別領(lǐng)域 ············································································· 6
1.3.3 自然語言理解領(lǐng)域 ····································································· 7
1.4 如何開展深度學(xué)習(xí)的研究和應(yīng)用開發(fā) ················································· 7
本章參考文獻(xiàn) ······························································································ 11
第2 章 國內(nèi)外深度學(xué)習(xí)技術(shù)研發(fā)現(xiàn)狀及其產(chǎn)業(yè)化趨勢 ······························· 13
2.1 Google 在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ·················································· 13
2.1.1 深度學(xué)習(xí)在Google 的應(yīng)用 ······················································ 13
2.1.2 Google 的TensorFlow 深度學(xué)習(xí)平臺 ······································ 14
2.1.3 Google 的深度學(xué)習(xí)芯片TPU ·················································· 15
2.2 Facebook 在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ·············································· 15
2.2.1 Torchnet ···················································································· 15
2.2.2 DeepText ··················································································· 16
2.3 百度在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ······················································· 17
2.3.1 光學(xué)字符識別 ··········································································· 17
2.3.2 商品圖像搜索 ··········································································· 17
2.3.3 在線廣告 ·················································································· 18
2.3.4 以圖搜圖 ·················································································· 18
2.3.5 語音識別 ·················································································· 18
2.3.6 百度開源深度學(xué)習(xí)平臺MXNet 及其改進(jìn)的深度語音識別系統(tǒng)Warp-CTC ····· 19
2.4 阿里巴巴在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ··············································· 19
2.4.1 拍立淘 ······················································································ 19
2.4.2 阿里小蜜——智能客服Messenger ········································· 20
2.5 京東在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ······················································· 20
2.6 騰訊在深度學(xué)習(xí)領(lǐng)域的研發(fā)現(xiàn)狀 ······················································· 21
2.7 科創(chuàng)型公司（基于深度學(xué)習(xí)的人臉識別系統(tǒng)） ······························· 22
2.8 深度學(xué)習(xí)的硬件支撐——NVIDIA GPU ············································ 23
本章參考文獻(xiàn) ······························································································ 24
深度學(xué)習(xí)理論篇
第3 章 神經(jīng)網(wǎng)絡(luò) ························································································· 30
3.1 神經(jīng)元的概念 ······················································································ 30
3.2 神經(jīng)網(wǎng)絡(luò) ····························································································· 31
3.2.1 后向傳播算法 ··········································································· 32
3.2.2 后向傳播算法推導(dǎo) ··································································· 33
3.3 神經(jīng)網(wǎng)絡(luò)算法示例 ·············································································· 36
本章參考文獻(xiàn) ······························································································ 38
第4 章 卷積神經(jīng)網(wǎng)絡(luò) ················································································· 39
4.1 卷積神經(jīng)網(wǎng)絡(luò)特性 ················································································ 39
4.1.1 局部連接 ·················································································· 40
4.1.2 權(quán)值共享 ·················································································· 41
4.1.3 空間相關(guān)下采樣 ······································································· 42
4.2 卷積神經(jīng)網(wǎng)絡(luò)操作 ·················4
展開全部
深度學(xué)習(xí)原理與應(yīng)用實(shí)踐 作者簡介
張重生，博士，教授，碩士生導(dǎo)師，河南大學(xué)大數(shù)據(jù)研究中心、大數(shù)據(jù)團(tuán)隊(duì)帶頭人。研究領(lǐng)域?yàn)榇髷?shù)據(jù)分析、深度學(xué)習(xí)、數(shù)據(jù)挖掘、數(shù)據(jù)庫、數(shù)據(jù)流（實(shí)時(shí)數(shù)據(jù)分析）。
博士畢業(yè)于 INRIA,France(法國國家信息與自動(dòng)化研究所)，獲得優(yōu)秀博士論文榮譽(yù)。2010年08月至2011年3月，在美國加州大學(xué)洛杉磯分校(UCLA)，計(jì)算機(jī)系，師從著名的數(shù)據(jù)庫專家Carlo Zaniolo教授，從事數(shù)據(jù)挖掘領(lǐng)域的合作研究。 2012-2013,挪威科技大學(xué)，ERCIM/Marie-Curie Fellow。
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