Python數(shù)據(jù)分析與應用

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