Python數(shù)據(jù)分析與應(yīng)用

前言 \n
\n
\n
\n
第1章Python簡介 \n
\n
11Python概要介紹 \n
\n
111Python主要特點(diǎn) \n
\n
112Python不足 \n
\n
113Python發(fā)展現(xiàn)狀 \n
\n
12Python發(fā)展歷史 \n
\n
121Python起源 \n
\n
122Python各版本 \n
\n
13Python常用工具包 \n
\n
14Python常見問題 \n
\n
141Python安裝 \n
\n
142Python IDE安裝 \n
\n
143Python和其他語言接口 \n
\n
144工具包的安裝 \n
\n
145工具包的導(dǎo)入 \n
\n
15Python在國內(nèi)的發(fā)展 \n
\n
151國內(nèi)鏡像 \n
\n
152中小學(xué)教育 \n
\n
153國內(nèi)使用Python情況 \n
\n
習(xí)題和作業(yè) \n
\n
第2章Python語法 \n
\n
21Python常用數(shù)據(jù)結(jié)構(gòu) \n
\n
211列表 \n
\n
212元組 \n
\n
213集合 \n
\n
214字典 \n
\n
22分支與循環(huán) \n
\n
221分支 \n
\n
222循環(huán) \n
\n
223三目表達(dá)式 \n
\n
23函數(shù)、類和模塊 \n
\n
231函數(shù) \n
\n
232類 \n
\n
233模塊 \n
\n
24Python語言與其他語言比較 \n
\n
習(xí)題和作業(yè) \n
\n
第3章Python科學(xué)計(jì)算 \n
\n
31Python基本計(jì)算 \n
\n
311算術(shù)運(yùn)算 \n
\n
312比較運(yùn)算 \n
\n
313賦值運(yùn)算 \n
\n
314邏輯運(yùn)算 \n
\n
315成員運(yùn)算符 \n
\n
316計(jì)算實(shí)例 \n
\n
32利用NumPy科學(xué)計(jì)算 \n
\n
321多維數(shù)組 \n
\n
322廣播特性 \n
\n
\n
\n
323遍歷軸 \n
\n
324數(shù)組操作 \n
\n
325矩陣運(yùn)算 \n
\n
326應(yīng)用案例——圖像壓縮 \n
\n
33Scipy包 \n
\n
331Scipy簡單介紹 \n
\n
332基本操作 \n
\n
333圖像處理 \n
\n
334快速傅里葉變換 \n
\n
335函數(shù)插值 \n
\n
336優(yōu)化 \n
\n
34NumPy與Python的性能比較 \n
\n
習(xí)題和作業(yè) \n
\n
第4章Python數(shù)據(jù)分析 \n
\n
41Pandas包 \n
\n
411讀入csv文件 \n
\n
412截取數(shù)據(jù)與描述數(shù)據(jù) \n
\n
413數(shù)據(jù)顯示 \n
\n
414數(shù)據(jù)處理 \n
\n
42Scikit-learn包 \n
\n
421特征降維 \n
\n
422聚類 \n
\n
423分類 \n
\n
43其他Python分析包 \n
\n
431頻譜分析 \n
\n
432時(shí)頻分析 \n
\n
433動(dòng)力學(xué)分析 \n
\n
習(xí)題和作業(yè) \n
\n
\n
\n
\n
第5章Python數(shù)據(jù)處理 \n
\n
51數(shù)據(jù)清洗 \n
\n
52數(shù)據(jù)預(yù)處理 \n
\n
53統(tǒng)計(jì)分析 \n
\n
54網(wǎng)絡(luò)數(shù)據(jù)采集 \n
\n
541網(wǎng)絡(luò)爬蟲 \n
\n
542網(wǎng)頁解析 \n
\n
55案例應(yīng)用 \n
\n
習(xí)題和作業(yè) \n
\n
第6章Python數(shù)據(jù)可視化 \n
\n
61可視化的基本概念 \n
\n
62利用Matplotlib進(jìn)行可視化 \n
\n
621繪制Matplotlib的圖表組成元素 \n
\n
622圖表的美化和修飾 \n
\n
63繪制統(tǒng)計(jì)圖形 \n
\n
64案例應(yīng)用 \n
\n
641氣溫?cái)?shù)據(jù)可視化分析 \n
\n
642交通線路圖可視化 \n
\n
習(xí)題和作業(yè) \n
\n
第7章Python圖像分析 \n
\n
71圖像分析簡介 \n
\n
72卷積神經(jīng)網(wǎng)絡(luò)組成 \n
\n
721卷積層 \n
\n
\n
722激活函數(shù)層 \n
\n
723池化層 \n
\n
724Dropout層 \n
\n
725Batch Normalization(BN)層 \n
\n
726全連接層 \n
\n
73經(jīng)典卷積網(wǎng)絡(luò)架構(gòu) \n
\n
731LeNet-5 \n
\n
732Alexlvet \n
\n
\n
\n
733VGGNet \n
\n
\n
\n
\n
734ResNet \n
\n
74案例應(yīng)用 \n
\n
741MNIST手寫數(shù)字識(shí)別 \n
\n
742Kaggle貓狗大戰(zhàn) \n
\n
75深度學(xué)習(xí)框架 \n
\n
習(xí)題和作業(yè) \n
\n
第8章Python視覺分析 \n
\n
81基于OpenCV的視頻操作 \n
\n
82目標(biāo)檢測(cè)簡介 \n
\n
83R-CNN系列發(fā)展歷程 \n
\n
84Faster R-CNN詳解 \n
\n
85YOLO系列發(fā)展歷程 \n
\n
86YOLOv4詳解 \n
\n
\n
\n
87案例應(yīng)用 \n
\n
871Faster R-CNN目標(biāo)檢測(cè) \n
\n
872YOLOv4目標(biāo)檢測(cè) \n
\n
88國內(nèi)視覺分析研究 \n
\n
881工業(yè)界 \n
\n
882學(xué)術(shù)界 \n
\n
習(xí)題和作業(yè) \n
\n
第9章Python時(shí)序分析 \n
\n
91時(shí)序分析介紹 \n
\n
92循環(huán)神經(jīng)網(wǎng)絡(luò) \n
\n
921記憶單元 \n
\n
922輸入輸出序列 \n
\n
923LSTM單元 \n
\n
924GRU單元 \n
\n
93案例應(yīng)用 \n
\n
931LSTM預(yù)測(cè)JetRail高鐵乘客 \n
\n
932GRU預(yù)測(cè)飛機(jī)乘客 \n
\n
933LSTM預(yù)測(cè)溫度 \n
\n
習(xí)題和作業(yè) \n
\n
第10章綜合案例 \n
\n
101人臉識(shí)別系統(tǒng) \n
\n
1011人臉數(shù)據(jù)收集 \n
\n
1012訓(xùn)練人臉分類器 \n
\n
1013人臉識(shí)別實(shí)現(xiàn) \n
\n
102PM25預(yù)測(cè)系統(tǒng) \n
\n
1021數(shù)據(jù)導(dǎo)入 \n
\n
1022建立數(shù)據(jù)集 \n
\n
1023構(gòu)造預(yù)測(cè)模型 \n
\n
1024模型訓(xùn)練與測(cè)試 \n
\n
參考文獻(xiàn)