深度學習:原理與應用實踐 深度学习:原理与应用实践
張重生
- 出版商: 電子工業
- 出版日期: 2016-12-01
- 定價: $288
- 售價: 7.9 折 $228
- 語言: 簡體中文
- 頁數: 220
- 裝訂: 平裝
- ISBN: 7121304139
- ISBN-13: 9787121304132
-
相關分類:
DeepLearning
銷售排行:
🥈 2017/2 簡體中文書 銷售排行 第 2 名
立即出貨 (庫存 < 4)
買這商品的人也買了...
-
$350$315 -
$780$616 -
$359$341 -
$648$616 -
$202深度學習:方法及應用
-
$490$417 -
$403深度學習 : 21天實戰 Caffe
-
$403解析深度學習 : 語音識別實踐
-
$474$450 -
$580$452 -
$301神經網絡與深度學習
-
$288深度學習導論及案例分析
-
$580$458 -
$403深度學習 : Caffe 之經典模型詳解與實戰
-
$590$502 -
$500$395 -
$360$281 -
$580$458 -
$500NLP 漢語自然語言處理原理與實踐
-
$403Tensorflow:實戰Google深度學習框架
-
$390$304 -
$480$379 -
$680$537 -
$590$460 -
$356深度學習算法實踐
相關主題
商品描述
本書全面、系統地介紹深度學習相關的技術,包括人工神經網絡,捲積神經網絡,深度學習平臺及源代碼分析,深度學習入門與進階,深度學習高級實踐,所有章節均附有源程序,所有實驗讀者均可重現,具有高度的可操作性和實用性。通過學習本書,研究人員、深度學習愛好者,能夠在3 個月內,系統掌握深度學習相關的理論和技術。
目錄大綱
深度學習基礎篇
第1章緒論········································· ·················································· ······· 2
1.1引言········································ ·················································· ············· 2
1.1.1 Google的深度學習成果···························· ································ 2
1.1.2 Microsoft的深度學習成果········· ················································ 3
1.1 .3國內公司的深度學習成果·········································· ··············· 3
1.2深度學習技術的發展歷程··························· ········································· 4
1.3深度學習的應用領域·· ·················································· ························ 6
1.3.1圖像識別領域··················· ·················································· ········ 6
1.3.2語音識別領域··································· ·········································· 6
1.3.3自然語言理解領域·················································· ··················· 7
1.4如何開展深度學習的研究和應用開發···················· ····························· 7
本章參考文獻················· ·················································· ··························· 11
第2章國內外深度學習技術研發現狀及其產業化趨勢······· ························ 13
2.1 Google在深度學習領域的研發現狀················ ·································· 13
2.1.1深度學習在Google的應用······ ················································ 13
2.1 .2 Google的TensorFlow深度學習平臺······································ 14
2.1.3 Google的深度學習芯片TPU ············································ ······ 15
2.2 Facebook在深度學習領域的研發現狀·································· ············ 15
2.2.1 Torchnet ································· ·················································· · 15
2.2.2 DeepText ············································ ······································· 16
2.3百度在深度學習領域的研發現狀· ·················································· ···· 17
2.3.1光學字符識別······································· ···································· 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習平臺MXNet及其改進的深度語音識別系統Warp-CTC ····· 19
2.4阿裡巴巴在深度學習領域的研發現狀····························· ·················· 19
2.4.1拍立淘························· ·················································· ··········· 19
2.4.2阿裡小蜜——智能客服Messenger ··························· ·············· 20
2.5京東在深度學習領域的研發現狀·························· ····························· 20
2.6騰訊在深度學習領域的研發現狀··········· ············································ 21
2.7科創型公司(基於深度學習的人臉識別系統) ······························· 22
2.8深度學習的硬件支撐—— NVIDIA GPU ············································ 23
本章參考文獻·················································· ············································ 24
深度學習理論篇
第3章神經網絡·············································· ··········································· 30
3.1神經元的概念· ·················································· ··································· 30
3.2神經網絡··········· ·················································· ································ 31
3.2.1後向傳播算法·········· ·················································· ··············· 32
3.2.2後向傳播算法推導·························· ········································· 33
3.3神經網絡算法示例··· ·················································· ························· 36
本章參考文獻····················· ·················································· ······················· 38
第4章捲積神經網絡··················· ·················································· ············ 39
4.1捲積神經網絡特性······························· ················································· 39
4.1.1局部連接············································· ····································· 40
4.1.2權值共享······ ·················································· ·························· 41
4.1.3空間相關下採樣················ ·················································· ····· 42
4.2捲積神經網絡操作······································ ········································ 42
4.2.1捲積操作··· ·················································· ····························· 42
4.2.2下採樣操作·············· ·················································· ·············· 44
4.3捲積神經網絡示例:LeNet-5 ························· ···································· 45
本章參考文獻·········· ·················································· ·································· 48
深度學習工具篇
第5章深度學習工具Caffe ···· ·················································· ·················· 50
5.1 Caffe的安裝··························· ·················································· ··········· 50
5.1.1安裝依賴包································ ·············································· 51
5.1.2 CUDA安裝················································ ······························ 51
5.1.3 MATLAB和Python安裝············ ············································ 54
5.1.4 OpenCV安裝(可選) ·············································· ·············· 59
5.1.5 Intel MKL或者BLAS安裝··························· ·························· 59
5.1.6 Caffe編譯和測試················ ·················································· ··· 59
5.1.7 Caffe安裝問題分析······································· ·························· 62
5.2 Caffe框架與源代碼解析················ ·················································· ·· 63
5.2.1數據層解析········································· ····································· 63
5.2.2網絡層解析······ ·················································· ······················ 74
5.2.3網絡結構解析····················· ·················································· ···· 92
5.2.4網絡求解解析······································· ·································· 104
本章參考文獻············ ·················································· ······························ 109
第6章深度學習工具Pylearn2 ············ ·················································· ·· 110
6.1 Pylearn2的安裝··········································· ······································· 110
6.1.1相關依賴安裝···· ·················································· ···················· 110
6.1.2安裝Pylearn2 ························ ·················································· 112
6.2 Pylearn2的使用············································· ····································· 112
本章參考文獻········· ·················································· ·································· 116
深度學習實踐篇(入門與進階)
第7章基於深度學習的手寫數字識別············································· ········· 118
7.1數據介紹····································· ·················································· ····· 118
7.1.1 MNIST數據集······································ ·································· 118
7.1.2提取MNIST數據集圖片······· ················································ 120
7.2手寫字體識別流程·············································· ······························ 121
7.2.1模型介紹·············· ·················································· ················ 121
7.2.2操作流程···························· ·················································· ·· 126
7.3實驗結果分析··········································· ········································· 127
本章參考文獻····· ·················································· ····································· 128
第8章基於深度學習的圖像識別··· ·················································· ········ 129
8.1數據來源······································ ·················································· ··· 129
8.1.1 Cifar10數據集介紹······································· ························· 129
8.1.2 Cifar10數據集格式················· ··············································· 129
8.2 Cifar10識別流程················································ ······························· 130
8.2.1模型介紹············· ·················································· ················· 130
8.2.2操作流程··························· ·················································· ··· 136
8.3實驗結果分析·········································· ············································ 139
本章參考文獻·· ·················································· ········································ 140
第9章基於深度學習的物體圖像識別················································· ····· 141
9.1數據來源········································· ·················································· 141
9.1.1 Caltech101數據集··········································· ······················· 141
9.1.2 Caltech101數據集處理··················· ······································· 142
9.2物體圖像識別流程····· ·················································· ····················· 143
9.2.1模型介紹······················· ·················································· ······· 143
9.2.2操作流程····································· ··········································· 144
9.3實驗結果分析·· ·················································· ··········