非程序员的0基础学员；经常与办公软件打交道的人；希望通过自动办公提升工作效率、减少重复劳动的人
课程推荐
《用Python自动办公，做职场高手》非程序员的0基础学员；经常与办公软件打交道的人；希望通过自动办公提升工作效率、减少重复劳动的人【技能收获】学完可掌握：AI Agent 智能体构建、Python 编程、Vue / 前端工程化、Docker / 容器化、MySQL / 数据库。课程以实战为导向，覆盖从基础概念到完整项目落地的关键步骤，配套章节笔记便于课后复盘与面试前快速回顾。【学习建议】建议按目录顺序学习，先打基础再进入综合实战章节；每完成 2～3 节可结合笔记整理一份学习小结，最终尝试独立复现一套完整 Demo 写入个人作品集。【就业发展】可面向岗位：相关领域工程师、技术专项人才、项目实战型开发者。系统学习并产出可展示的项目成果，是技术岗跳槽与晋升的有效路径；完整课程目录有助于制定阶段性学习计划并在简历中体现技术深度。 若你已有一定编程或运维基础，本课程可帮助你在现有技能栈上快速叠加热门方向能力，提升求职时的项目说服力与薪资谈判空间。
视频目录（828 节）
用Python自动办公，做职场高手
* ┃ ┣━━1.1物理预测的胜利与失效.mp4 [56.8M]
* ┃ ┣━━1.2预测失效原因.mp4 [19.9M]
* ┃ ┣━━1.3复杂系统引论.mp4 [40.7M]
* ┃ ┗━━1.4生活实例与本章答疑.mp4 [36M]
* ┃ ┣━━2.1大数据预测因为.mp4 [36.9M]
* ┃ ┗━━2.2大数据与机器学习.mp4 [11.1M]
* ┃ ┣━━3.10课程大纲（二）.mp4 [31.6M]
* ┃ ┣━━3.1规则阶段.mp4 [100.8M]
* ┃ ┣━━3.2机器学习阶段发展至连接主义阶段.mp4 [18.6M]
* ┃ ┣━━3.3课间答疑.mp4 [175.6M]
* ┃ ┣━━3.4连接主义阶段发展至学习阶段.mp4 [49.4M]
* ┃ ┣━━3.5三个阶段总结分析.mp4 [22.1M]
* ┃ ┣━━3.6人工智能的应用（一）.mp4 [43.3M]
* ┃ ┣━━3.7人工智能的应用（二）.mp4 [24.6M]
* ┃ ┣━━3.8课间答疑.mp4 [169.5M]
* ┃ ┗━━3.9课程大纲（一）.mp4 [38.2M]
* ┃ ┣━━4.3实数的定义（三）.mp4 [36.7M]
* ┃ ┣━━4.10级数的收敛.mp4 [47.8M]
* ┃ ┣━━4.12极限的四则运算.mp4 [33.3M]
* ┃ ┣━━4.13极限的复合.mp4 [25.1M]
* ┃ ┣━━4.14连续性.mp4 [40.5M]
* ┃ ┣━━4.1实数的定义（一）.mp4 [32.8M]
* ┃ ┣━━4.2实数的定义（二）.mp4 [41.8M]
* ┃ ┣━━4.11极限的定义.mp4 [39.1M]
* ┃ ┣━━4.4实数的元素个数（一）.mp4 [22.7M]
* ┃ ┣━━4.5实数的元素个数（二）.mp4 [37.5M]
* ┃ ┣━━4.6自然数个数少于实数个数（一）.mp4 [38.6M]
* ┃ ┣━━4.7自然数个数少于实数个数（二）.mp4 [44.6M]
* ┃ ┣━━4.8无穷大之比较（一）.mp4 [44.1M]
* ┃ ┗━━4.9无穷大之比较（二）.mp4 [25M]
* ┃ ┣━━5.1用网络的思维看经济结构.mp4 [46.1M]
* ┃ ┣━━5.2复杂网络认识前后.mp4 [56.3M]
* ┃ ┣━━5.3从网络结构看不同地区（一）.mp4 [75.1M]
* ┃ ┗━━5.4从网络结构看不同地区（二）.mp4 [41.4M]
* ┃ ┣━━6.1什么是机器学习.mp4 [24.5M]
* ┃ ┣━━6.2机器学习的类型.mp4 [44.1M]
* ┃ ┣━━6.3简单回归实例（一）.mp4 [43M]
* ┃ ┣━━6.4简单回归实例（二）.mp4 [34.3M]
* ┃ ┗━━6.5简单回归实例（三）.mp4 [180.7M]
* ┃ ┣━━7.1人工智能的发展.mp4 [41.8M]
* ┃ ┣━━7.2强化学习算法（一）.mp4 [31.4M]
* ┃ ┣━━7.3强化学习算法（二）.mp4 [50.4M]
* ┃ ┣━━7.4强化学习算法（三）.mp4 [33M]
* ┃ ┣━━7.5Alphago给我们的启示.mp4 [21M]
* ┃ ┗━━7.6无监督学习.mp4 [25.6M]
* ┃ ┣━━8.1元素与极限的知识点回顾.mp4 [40.4M]
* ┃ ┣━━8.2第一个重要极限定理的证明（一）.mp4 [38.7M]
* ┃ ┣━━8.3第一个重要极限定理的证明（二）.mp4 [26.4M]
* ┃ ┣━━8.4夹逼定理.mp4 [24.8M]
* ┃ ┗━━8.5第二个重要极限定理的证明.mp4 [27.8M]
* ┃ ┣━━9.10泰勒展开的证明.mp4 [37.6M]
* ┃ ┣━━9.1导数的定义.mp4 [38.6M]
* ┃ ┣━━9.2初等函数的导数.mp4 [45.3M]
* ┃ ┣━━9.3反函数的导数（一）.mp4 [20.9M]
* ┃ ┣━━9.4反函数的导数（二）.mp4 [26.9M]
* ┃ ┣━━9.5复合函数的导数.mp4 [28.7M]
* ┃ ┣━━9.6泰勒展开.mp4 [17M]
* ┃ ┣━━9.7罗尔定理.mp4 [25.7M]
* ┃ ┣━━9.8微分中值定理和柯西中值定理.mp4 [52.4M]
* ┃ ┗━━9.9洛比塔法则.mp4 [45.1M]
* ┃ ┣━━10.3概率基础【微信：17358309816】.mp4 [35.6M]
* ┃ ┣━━10.10贝叶斯于机器学习（一）.mp4 [47.6M]
* ┃ ┣━━10.12贝叶斯决策（一）.mp4 [34.8M]
* ┃ ┣━━10.13贝叶斯决策（二）.mp4 [45M]
* ┃ ┣━━10.14贝叶斯决策（三）.mp4 [65.3M]
* ┃ ┣━━10.1梯度优化（一）.mp4 [61M]
* ┃ ┣━━10.2梯度优化（二）.mp4 [67.8M]
* ┃ ┣━━10.11贝叶斯于机器学习（二）.mp4 [20.5M]
* ┃ ┣━━10.4概率与事件.mp4 [37.4M]
* ┃ ┣━━10.5贝叶斯推理（一）.mp4 [35.9M]
* ┃ ┣━━10.6贝叶斯推理（二）.mp4 [37.1M]
* ┃ ┣━━10.7贝叶斯推理（三）.mp4 [31M]
* ┃ ┣━━10.8辛普森案件【微信：17358309816】.mp4 [52.4M]
* ┃ ┗━━10.9贝叶斯推理深入.mp4 [43.6M]
* ┃ ┣━━11.1泰勒展开.mp4 [41.1M]
* ┃ ┣━━11.2展开半径.mp4 [27.6M]
* ┃ ┣━━11.3欧拉公式.mp4 [48.3M]
* ┃ ┣━━11.4泰勒展开求极限（一）.mp4 [27.1M]
* ┃ ┗━━11.5泰勒展开求极限（二）.mp4 [57.2M]
* ┃ ┣━━12.1偏导数的对称性.mp4 [34.8M]
* ┃ ┣━━12.2链式法则.mp4 [34.3M]
* ┃ ┗━━12.3梯度算符、拉氏算符.mp4 [68.6M]
* ┃ ┣━━13.1黎曼积.mp4 [22.4M]
* ┃ ┣━━13.2微积分基本定理.mp4 [54.3M]
* ┃ ┣━━13.3分部积分（一）.mp4 [46.5M]
* ┃ ┗━━13.4分部积分（二）.mp4 [38.8M]
* ┃ ┣━━14.1标准正态分布.mp4 [49.1M]
* ┃ ┣━━14.2中心极限定理.mp4 [34.1M]
* ┃ ┣━━14.3误差函数.mp4 [28.9M]
* ┃ ┣━━14.4二维正态分布.mp4 [44.1M]
* ┃ ┗━━14.5多维正态分布.mp4 [32.8M]
* ┃ ┣━━15.4先验到后验的过程.mp4 [21.7M]
* ┃ ┣━━15.10朴素贝叶斯（三）.mp4 [63.8M]
* ┃ ┣━━15.12最大似然估计（二）.mp4 [51.4M]
* ┃ ┣━━15.1蒙特卡洛分析（一）.mp4 [49.2M]
* ┃ ┣━━15.2蒙特卡洛分析（二）.mp4 [34.8M]
* ┃ ┣━━15.3贝叶斯先验.mp4 [47.4M]
* ┃ ┣━━15.11最大似然估计（一）.mp4 [24.7M]
* ┃ ┣━━15.5朴素贝叶斯（一）.mp4 [33.5M]
* ┃ ┣━━15.6朴素贝叶斯（二）.mp4 [40.5M]
* ┃ ┣━━15.7算法设计.mp4 [22.3M]
* ┃ ┣━━15.8TF-IDF（一）.mp4 [47.2M]
* ┃ ┗━━15.9TF-IDF（二）.mp4 [40M]
* ┃ ┣━━16.4线空间.mp4 [17M]
* ┃ ┣━━16.10常规线空间.mp4 [52.4M]
* ┃ ┣━━16.12秩.mp4 [55M]
* ┃ ┣━━16.1线代数概述.mp4 [36.2M]
* ┃ ┣━━16.2线代数应用方法论.mp4 [17.4M]
* ┃ ┣━━16.3线律.mp4 [44.4M]
* ┃ ┣━━16.11线关.mp4 [36.3M]
* ┃ ┣━━16.5线空间八条法则（一）.mp4 [51.5M]
* ┃ ┣━━16.6线空间八条法则（二）.mp4 [46.9M]
* ┃ ┣━━16.7线空间八条法则（三）.mp4 [31.5M]
* ┃ ┣━━16.8连续傅.mp4 [26.8M]
* ┃ ┗━━16.9傅立.mp4 [41.3M]
* ┃ ┣━━17.3回顾统计学（二）.mp4 [52.9M]
* ┃ ┣━━17.10随机变量（二）.mp4 [15.3M]
* ┃ ┣━━17.12换门的概率模拟计算（二）.mp4 [37.8M]
* ┃ ┣━━17.13换门的概率模拟计算（三）.mp4 [50.5M]
* ┃ ┣━━17.1课程Overview.mp4 [36.4M]
* ┃ ┣━━17.2回顾统计学（一）.mp4 [65.3M]
* ┃ ┣━━17.11换门的概率模拟计算（一）.mp4 [59M]
* ┃ ┣━━17.4回顾统计学（三）.mp4 [28.1M]
* ┃ ┣━━17.5回顾数据科学（一）.mp4 [33.8M]
* ┃ ┣━━17.6回顾数据科学（二）和教材介绍.mp4 [60.8M]
* ┃ ┣━━17.7R和RStudio等介绍（一）.mp4 [25M]
* ┃ ┣━━17.8R和RStudio等介绍（二）.mp4 [29.7M]
* ┃ ┗━━17.9随机变量（一）.mp4 [21.3M]
* ┃ ┣━━18.3可矩阵表示坐标变化.mp4 [64.9M]
* ┃ ┣━━18.10等价类.mp4 [57.4M]
* ┃ ┣━━18.12行列式（二）.mp4 [38.2M]
* ┃ ┣━━18.13行列式（三）.mp4 [52.4M]
* ┃ ┣━━18.1线代数知识点回顾.mp4 [32.1M]
* ┃ ┣━━18.2矩阵表示线变化.mp4 [31.3M]
* ┃ ┣━━18.11行列式（一）.mp4 [28.3M]
* ┃ ┣━━18.4相似矩阵.mp4 [68.2M]
* ┃ ┣━━18.5相似矩阵表示相同线变化.mp4 [22.8M]
* ┃ ┣━━18.6线代数解微分方程.mp4 [67.4M]
* ┃ ┣━━18.7矩阵的运算—转秩（一）.mp4 [41.9M]
* ┃ ┣━━18.8矩阵的运算—转秩（二）.mp4 [34.8M]
* ┃ ┗━━18.9等价关系.mp4 [30.7M]
* ┃ ┣━━19.2Python介绍（二）.mp4 [39.9M]
* ┃ ┣━━19.10变量类型—字符串类型（三）.mp4 [42.4M]
* ┃ ┣━━19.12变量类型—列表类型（二）.mp4 [39.6M]
* ┃ ┣━━19.13变量类型—列表类型（三）.mp4 [21.4M]
* ┃ ┣━━19.14变量类型—语言组类型、字典类型（一）.mp4 [29.9M]
* ┃ ┣━━19.15变量类型—字典类型（二）.mp4 [32M]
* ┃ ┣━━19.1Python介绍（一）.mp4 [31.6M]
* ┃ ┣━━19.11变量类型—列表类型（一）.mp4 [25.4M]
* ┃ ┣━━19.3变量—命名规范.mp4 [30.5M]
* ┃ ┣━━19.4变量—代码规范.mp4 [21.2M]
* ┃ ┣━━19.5变量类型—数值类型.mp4 [23.5M]
* ┃ ┣━━19.6变量类型—bool类型.mp4 [21.1M]
* ┃ ┣━━19.7变量类型—字符串类型（一）.mp4 [27.3M]
* ┃ ┣━━19.8课间答疑.mp4 [21.4M]
* ┃ ┗━━19.9变量类型—字符串类型（二）.mp4 [33.5M]
* ┃ ┣━━20.3例题讲解（二）.mp4 [34.4M]
* ┃ ┣━━20.10线代数核心定理.mp4 [28.7M]
* ┃ ┣━━20.12对偶空间（二）.mp4 [46.7M]
* ┃ ┣━━20.13欧氏空间与闵氏空间.mp4 [23.5M]
* ┃ ┣━━20.14厄米矩阵.mp4 [11.4M]
* ┃ ┣━━20.1线代数知识点回顾.mp4 [29.1M]
* ┃ ┣━━20.2例题讲解（一）.mp4 [34.5M]
* ┃ ┣━━20.11对偶空间（一）.mp4 [28.6M]
* ┃ ┣━━20.4例题讲解（三）.mp4 [40.6M]
* ┃ ┣━━20.5特征值与特征向量的物理意义.mp4 [68.8M]
* ┃ ┣━━20.6特征值与特征向量的性质（一）.mp4 [16.8M]
* ┃ ┣━━20.7特征值与特征向量的性质（二）.mp4 [47.6M]
* ┃ ┣━━20.8本征值的计算（一）.mp4 [31.4M]
* ┃ ┗━━20.9本征值的计算（二）.mp4 [31.8M]
* ┃ ┣━━21.3正则化.mp4 [18.8M]
* ┃ ┣━━21.10KNN(K最近邻)算法（二）.mp4 [39.8M]
* ┃ ┣━━21.12线性分类器.mp4 [28.4M]
* ┃ ┣━━21.13高斯判别模型（一）.mp4 [23.8M]
* ┃ ┣━━21.14高斯判别模型（二）.mp4 [34.2M]
* ┃ ┣━━21.1经验误差和泛化误差.mp4 [43.3M]
* ┃ ┣━━21.2最大后验估计.mp4 [42.6M]
* ┃ ┣━━21.11KNN(K最近邻)算法（三）.mp4 [19.8M]
* ┃ ┣━━21.4lasso回归.mp4 [45.8M]
* ┃ ┣━━21.5超参数（一）.mp4 [34.3M]
* ┃ ┣━━21.6超参数（二）.mp4 [26.8M]
* ┃ ┣━━21.7监督学习框架（一）.mp4 [32.1M]
* ┃ ┣━━21.8监督学习框架（二）.mp4 [42.4M]
* ┃ ┗━━21.9KNN(K最近邻)算法（一）.mp4 [36.2M]
* ┃ ┣━━22.3循环（一）.mp4 [16.6M]
* ┃ ┣━━22.10函数（三）.mp4 [28.5M]
* ┃ ┣━━22.12类（一）.mp4 [29.4M]
* ┃ ┣━━22.13类（二）.mp4 [26.8M]
* ┃ ┣━━22.14类（三）.mp4 [24.8M]
* ┃ ┣━━22.1条件判断（一）.mp4 [36.4M]
* ┃ ┣━━22.2条件判断（二）.mp4 [32.6M]
* ┃ ┣━━22.11函数（四）.mp4 [33.9M]
* ┃ ┣━━22.4循环（二）.mp4 [25.6M]
* ┃ ┣━━22.5课间答疑.mp4 [25.6M]
* ┃ ┣━━22.6循环（三）.mp4 [24.8M]
* ┃ ┣━━22.7循环（四）.mp4 [30.1M]
* ┃ ┣━━22.8函数（一）.mp4 [18.5M]
* ┃ ┗━━22.9函数（二）.mp4 [24.5M]
* ┃ ┣━━23.1无监督学习框架.mp4 [25.4M]
* ┃ ┣━━23.2降维存在的原因.mp4 [21.3M]
* ┃ ┣━━23.3PCA数学分析方法（一）.mp4 [31.3M]
* ┃ ┣━━23.4PCA数学分析方法（二）.mp4 [41.7M]
* ┃ ┣━━23.5PCA数学分析方法（三）.mp4 [29.1M]
* ┃ ┣━━23.6PCA数学分析方法（四）.mp4 [34.1M]
* ┃ ┣━━23.7PCA之外的降维方法—LDA.mp4 [16.3M]
* ┃ ┣━━23.8PCA背后的假设（一）.mp4 [41.9M]
* ┃ ┗━━23.9PCA背后的假设（二）.mp4 [49.6M]
* ┃ ┣━━24.3理解统计思想（二）.mp4 [54M]
* ┃ ┣━━24.10参数估计（一）.mp4 [26.4M]
* ┃ ┣━━24.12假设检验（一）.mp4 [16.3M]
* ┃ ┣━━24.13假设检验（二）.mp4 [23.4M]
* ┃ ┣━━24.1课程Overview.mp4 [21.3M]
* ┃ ┣━━24.2理解统计思想（一）.mp4 [22.2M]
* ┃ ┣━━24.11参数估计（二）.mp4 [20.7M]
* ┃ ┣━━24.4理解统计思想（三）.mp4 [21.8M]
* ┃ ┣━━24.5概率空间.mp4 [14.8M]
* ┃ ┣━━24.6随机变量（一）.mp4 [32.3M]
* ┃ ┣━━24.7随机变量（二）.mp4 [16.8M]
* ┃ ┣━━24.8随机变量（三）.mp4 [45M]
* ┃ ┗━━24.9随机变量（四）.mp4 [12.4M]
* ┃ ┣━━25.3认识关系型数据库（二）.mp4 [45.1M]
* ┃ ┣━━25.10Python操作数据库（二）.mp4 [39.1M]
* ┃ ┣━━25.12Python操作数据库（四）.mp4 [47.9M]
* ┃ ┣━━25.13Python爬虫（一）.mp4 [65.3M]
* ┃ ┣━━25.14Python爬虫（二）.mp4 [84.9M]
* ┃ ┣━━25.15Python爬虫（三）.mp4 [59.2M]
* ┃ ┣━━25.16Python爬虫（四）.mp4 [57.8M]
* ┃ ┣━━25.17Python爬虫（五）.mp4 [69.9M]
* ┃ ┣━━25.1课程介绍.mp4 [23M]
* ┃ ┣━━25.2认识关系型数据库（一）.mp4 [46M]
* ┃ ┣━━25.11Python操作数据库（三）.mp4 [22.9M]
* ┃ ┣━━25.4MySQL数据库与Excel的不同.mp4 [25.9M]
* ┃ ┣━━25.5命令行操作数据库（一）.mp4 [43.9M]
* ┃ ┣━━25.6命令行操作数据库（二）.mp4 [41M]
* ┃ ┣━━25.7命令行操作数据库（三）.mp4 [19.8M]
* ┃ ┣━━25.8命令行操作数据库（四）.mp4 [39.6M]
* ┃ ┣━━25.9Python操作数据库（一）.mp4 [32.9M]
* ┃ ┣━━26.3Lasso：alpha参数与准确率（三）.mp4 [62.1M]
* ┃ ┣━━26.10Perceptron（三）.mp4 [31.5M]
* ┃ ┣━━26.12熵与信息（一）.mp4 [22.9M]
* ┃ ┣━━26.13熵与信息（二）.mp4 [25.3M]
* ┃ ┣━━26.1Lasso：alpha参数与准确率（一）.mp4 [24.7M]
* ┃ ┣━━26.2Lasso：alpha参数与准确率（二）.mp4 [16M]
* ┃ ┣━━26.11Perceptron（四）.mp4 [31M]
* ┃ ┣━━26.4线分类器.mp4 [24M]
* ┃ ┣━━26.5LDA（一）.mp4 [24.8M]
* ┃ ┣━━26.6LDA（二）.mp4 [27.1M]
* ┃ ┣━━26.7LDA（三）.mp4 [32.6M]
* ┃ ┣━━26.8Perceptron（一）.mp4 [45.2M]
* ┃ ┗━━26.9Perceptron（二）.mp4 [29.1M]
* ┃ ┣━━27.3NumPy基本操作（三）.mp4 [27.4M]
* ┃ ┣━━27.10Pandas基本操作（四）.mp4 [26.1M]
* ┃ ┣━━27.12Pandas绘图（二）.mp4 [37.9M]
* ┃ ┣━━27.13Pandas绘图（三）【微信：17358309816】.mp4 [23.5M]
* ┃ ┣━━27.14Pandas绘图（四）.mp4 [46.9M]
* ┃ ┣━━27.1NumPy基本操作（一）.mp4 [31M]
* ┃ ┣━━27.2NumPy基本操作（二）.mp4 [24.5M]
* ┃ ┣━━27.11Pandas绘图（一）.mp4 [34.4M]
* ┃ ┣━━27.4NumPy基本操作（四）.mp4 [18.2M]
* ┃ ┣━━27.5NumPy基本操作（五）.mp4 [28.9M]
* ┃ ┣━━27.6NumPy基本操作（六）.mp4 [25.7M]
* ┃ ┣━━27.7Pandas基本操作（一）.mp4 [42.1M]
* ┃ ┣━━27.8Pandas基本操作（二）【微信：17358309816】.mp4 [35M]
* ┃ ┗━━27.9Pandas基本操作（三）.mp4 [38.4M]
* ┃ ┣━━28.1课程介绍.mp4 [29.7M]
* ┃ ┣━━28.2Scikit-Learn介绍.mp4 [12.6M]
* ┃ ┣━━28.3数据处理（一）【微信：zhifuwz178】.mp4 [38.7M]
* ┃ ┣━━28.4数据处理（二）.mp4 [54.6M]
* ┃ ┣━━28.5模型实例、模型选择（一）.mp4 [37.9M]
* ┃ ┣━━28.6模型实例、模型选择（二）.mp4 [24.2M]
* ┃ ┣━━28.7模型实例、模型选择（三）.mp4 [21.3M]
* ┃ ┣━━28.8模型实例、模型选择（四）.mp4 [45.1M]
* ┃ ┗━━28.9模型实例、模型选择（五）.mp4 [32.2M]
* ┃ ┣━━29.3熵（三）.mp4 [30.2M]
* ┃ ┣━━29.10逻辑斯蒂回归（三）.mp4 [40.6M]
* ┃ ┣━━29.12逻辑斯蒂回归（五）.mp4 [24.4M]
* ┃ ┣━━29.13SVM引入.mp4 [15.5M]
* ┃ ┣━━29.1熵（一）.mp4 [35.9M]
* ┃ ┣━━29.2熵（二）.mp4 [38.1M]
* ┃ ┣━━29.11逻辑斯蒂回归（四）.mp4 [40.6M]
* ┃ ┣━━29.4熵（四）.mp4 [32.7M]
* ┃ ┣━━29.5熵（五）.mp4 [20.2M]
* ┃ ┣━━29.6熵（六）.mp4 [31.3M]
* ┃ ┣━━29.7熵（七）.mp4 [10.8M]
* ┃ ┣━━29.8逻辑斯蒂回归（一）.mp4 [38.7M]
* ┃ ┗━━29.9逻辑斯蒂回归（二）.mp4 [37.5M]
* ┃ ┣━━30.1泰坦尼克数据处理与分析（一）.mp4 [29.3M]
* ┃ ┣━━30.2泰坦尼克数据处理与分析（二）.mp4 [22.1M]
* ┃ ┣━━30.3泰坦尼克数据处理与分析（三）.mp4 [22.3M]
* ┃ ┣━━30.4泰坦尼克数据处理与分析（四）.mp4 [28.4M]
* ┃ ┣━━30.5泰坦尼克数据处理与分析（五）.mp4 [27.5M]
* ┃ ┣━━30.6泰坦尼克数据处理与分析（六）.mp4 [20.7M]
* ┃ ┣━━30.7泰坦尼克数据处理与分析（七）.mp4 [38.1M]
* ┃ ┣━━30.8泰坦尼克数据处理与分析（八）.mp4 [40.5M]
* ┃ ┗━━30.9泰坦尼克数据处理与分析（九）.mp4 [41.5M]
* ┃ ┣━━31.1决策树（一）.mp4 [21.3M]
* ┃ ┣━━31.2决策树（二）.mp4 [32.6M]
* ┃ ┣━━31.3决策树（三）.mp4 [37M]
* ┃ ┗━━31.4决策树（四）.mp4 [27.1M]
* ┃ ┣━━32.1课程安排.mp4 [48.8M]
* ┃ ┣━━32.2什么是数据可视化.mp4 [15.8M]
* ┃ ┣━━32.3设计原则.mp4 [24M]
* ┃ ┣━━32.4数据可视化流程.mp4 [24.4M]
* ┃ ┣━━32.5视觉编码.mp4 [34.1M]
* ┃ ┣━━32.6图形选择（一）.mp4 [26.5M]
* ┃ ┣━━32.7图形选择（二）.mp4 [20.2M]
* ┃ ┗━━32.8图形选择（三）.mp4 [22.5M]
* ┃ ┣━━33.1Hadoop介绍.mp4 [30.2M]
* ┃ ┣━━33.2Hdfs应用（一）.mp4 [70.6M]
* ┃ ┣━━33.3Hdfs应用（二）.mp4 [59.6M]
* ┃ ┣━━33.4MapReduce（一）.mp4 [41.3M]
* ┃ ┣━━33.5MapReduce（二）.mp4 [27.8M]
* ┃ ┣━━33.6Hive应用（一）.mp4 [69.5M]
* ┃ ┣━━33.7Hive应用（二）.mp4 [82.5M]
* ┃ ┣━━33.8Hive应用（三）.mp4 [103.4M]
* ┃ ┗━━33.9Hive应用（四）.mp4 [87.2M]
* ┃ ┣━━34.3Pig应用（三）.mp4 [62.5M]
* ┃ ┣━━34.10Spark应用（四）.mp4 [79M]
* ┃ ┣━━34.12Spark应用（六）.mp4 [118.6M]
* ┃ ┣━━34.13Spark应用（七）.mp4 [102.7M]
* ┃ ┣━━34.1Pig应用（一）.mp4 [60.7M]
* ┃ ┣━━34.2Pig应用（二）.mp4 [57.6M]
* ┃ ┣━━34.11Spark应用（五）.mp4 [94.6M]
* ┃ ┣━━34.4Pig应用（四）.mp4 [58.2M]
* ┃ ┣━━34.5Pig应用（五）.mp4 [55.6M]
* ┃ ┣━━34.6Pig应用（六）.mp4 [25M]
* ┃ ┣━━34.7Spark应用（一）.mp4 [70.7M]
* ┃ ┣━━34.8Spark应用（二）.mp4 [38.4M]
* ┃ ┗━━34.9Spark应用（三）.mp4 [98.8M]
* ┃ ┣━━35.1推荐技术的介绍.mp4 [24.5M]
* ┃ ┣━━35.2人是如何推荐商品的.mp4 [24.7M]
* ┃ ┣━━35.3推荐系统的形式化以及如何评价推荐结果.mp4 [17.3M]
* ┃ ┣━━35.4求解—从数据到模型.mp4 [23.8M]
* ┃ ┣━━35.5数据拆分与特征工程.mp4 [26.8M]
* ┃ ┣━━35.6推荐系统机器学习模型.mp4 [35.1M]
* ┃ ┣━━35.7评估模型.mp4 [24.8M]
* ┃ ┗━━35.8建模过程的演示与课间答疑.mp4 [29.7M]
* ┃ ┣━━36.2随机森林.mp4 [29.7M]
* ┃ ┣━━36.10Bagging与决策树（一）.mp4 [25.4M]
* ┃ ┣━━36.12Boosting方法（一）.mp4 [31.6M]
* ┃ ┣━━36.13Boosting方法（二）.mp4 [17.3M]
* ┃ ┣━━36.14Boosting方法（三）.mp4 [35.5M]
* ┃ ┣━━36.15Boosting方法（四）.mp4 [30M]
* ┃ ┣━━36.1决策树.mp4 [16.7M]
* ┃ ┣━━36.11Bagging与决策树（二）.mp4 [29.9M]
* ┃ ┣━━36.3在Scikit-Learn里面如何用随机森林做预测（一）.mp4 [35.6M]
* ┃ ┣━━36.4在Scikit-Learn里面如何用随机森林做预测（二）.mp4 [36M]
* ┃ ┣━━36.5模型参数的介绍.mp4 [26.4M]
* ┃ ┣━━36.6集成方法（一）.mp4 [28M]
* ┃ ┣━━36.7集成方法（二）.mp4 [26M]
* ┃ ┣━━36.8Blending.mp4 [17.4M]
* ┃ ┗━━36.9gt多样化.mp4 [17.6M]
* ┃ ┣━━37.2静态信息图（二）.mp4 [34.1M]
* ┃ ┣━━37.10D3（三）.mp4 [24.1M]
* ┃ ┣━━37.12svg.html.mp4 [68.3M]
* ┃ ┣━━37.13D3支持的数据类型.mp4 [59.4M]
* ┃ ┣━━37.14Make a map（一）.mp4 [57.5M]
* ┃ ┣━━37.15Make a map（二）.mp4 [17.9M]
* ┃ ┣━━37.1静态信息图（一）.mp4 [24.8M]
* ┃ ┣━━37.11div.html.mp4 [20.9M]
* ┃ ┣━━37.3静态信息图（三）.mp4 [61.2M]
* ┃ ┣━━37.4静态信息图（四）.mp4 [38.4M]
* ┃ ┣━━37.5静态信息图（五）.mp4 [41.6M]
* ┃ ┣━━37.6HTML、CSS和JavaScript基础介绍.mp4 [48.3M]
* ┃ ┣━━37.7DOM和开发者工具.mp4 [28.7M]
* ┃ ┣━━37.8D3（一）.mp4 [40.3M]
* ┃ ┗━━37.9D3（二）.mp4 [40.4M]
* ┃ ┣━━38.3经典条件反射（二）.mp4 [29.5M]
* ┃ ┣━━38.10Policy Learning（二）.mp4 [23.5M]
* ┃ ┣━━38.12Policy Learning（四）.mp4 [27.7M]
* ┃ ┣━━38.13Policy Learning（五）.mp4 [17.6M]
* ┃ ┣━━38.14Policy Learning（六）.mp4 [37.1M]
* ┃ ┣━━38.1你所了解的强化学习是什么.mp4 [27.7M]
* ┃ ┣━━38.2经典条件反射（一）.mp4 [17.5M]
* ┃ ┣━━38.11Policy Learning（三）.mp4 [33M]
* ┃ ┣━━38.4操作性条件反射.mp4 [27.8M]
* ┃ ┣━━38.5Evaluation Problem（一）.mp4 [26.5M]
* ┃ ┣━━38.6Evaluation Problem（二）.mp4 [14.8M]
* ┃ ┣━━38.7Evaluation Problem（三）.mp4 [20M]
* ┃ ┣━━38.8Evaluation Problem（四）.mp4 [30.8M]
* ┃ ┗━━38.9Policy Learning（一）.mp4 [23.3M]
* ┃ ┣━━39.2基于模型的RL（一）.mp4 [37.3M]
* ┃ ┣━━39.10大脑中的强化学习算法（三）.mp4 [13.1M]
* ┃ ┣━━39.12大脑中的强化学习算法（五）.mp4 [25.5M]
* ┃ ┣━━39.13RL in alphaGo（一）.mp4 [27.5M]
* ┃ ┣━━39.14RL in alphaGo（二）.mp4 [27.9M]
* ┃ ┣━━39.15RL in alphaGo（三）.mp4 [18.1M]
* ┃ ┣━━39.16RL in alphaGo（四）.mp4 [42.3M]
* ┃ ┣━━39.1Policy Learning总结.mp4 [25.1M]
* ┃ ┣━━39.11大脑中的强化学习算法（四）.mp4 [24.7M]
* ┃ ┣━━39.3基于模型的RL（二）.mp4 [14.1M]
* ┃ ┣━━39.4基于模型的RL（三）.mp4 [36.6M]
* ┃ ┣━━39.5基于模型的RL（四）.mp4 [36.5M]
* ┃ ┣━━39.6基于模型的RL（五）.mp4 [20M]
* ┃ ┣━━39.7基于模型的RL（六）.mp4 [16.2M]
* ┃ ┣━━39.8大脑中的强化学习算法（一）.mp4 [38.3M]
* ┃ ┗━━39.9大脑中的强化学习算法（二）.mp4 [21.1M]
* ┃ ┣━━40.3SVM（二）.mp4 [45.9M]
* ┃ ┣━━40.10SVM（九）.mp4 [37.6M]
* ┃ ┣━━40.12SVM（十一）.mp4 [45.3M]
* ┃ ┣━━40.13SVM（十二）和网络引入.mp4 [52M]
* ┃ ┣━━40.1VC维.mp4 [34.1M]
* ┃ ┣━━40.2SVM（一）.mp4 [37.2M]
* ┃ ┣━━40.11SVM（十）.mp4 [48.4M]
* ┃ ┣━━40.4SVM（三）.mp4 [27.9M]
* ┃ ┣━━40.5SVM（四）.mp4 [40.2M]
* ┃ ┣━━40.6SVM（五）.mp4 [36M]
* ┃ ┣━━40.7SVM（六）.mp4 [29.5M]
* ┃ ┣━━40.8SVM（七）.mp4 [24.7M]
* ┃ ┗━━40.9SVM（八）.mp4 [55.8M]
* ┃ ┣━━41.2集成模型总结（二）.mp4 [41M]
* ┃ ┣━━41.10GDBT理解及其衍生应用（五）.mp4 [43.2M]
* ┃ ┣━━41.12GDBT理解及其衍生应用（七）.mp4 [43.9M]
* ┃ ┣━━41.13GDBT理解及其衍生应用（八）.mp4 [87.3M]
* ┃ ┣━━41.14GDBT理解及其衍生应用（九）.mp4 [28.8M]
* ┃ ┣━━41.15GDBT理解及其衍生应用（十）.mp4 [60.8M]
* ┃ ┣━━41.1集成模型总结（一）.mp4 [38.1M]
* ┃ ┣━━41.11GDBT理解及其衍生应用（六）.mp4 [49M]
* ┃ ┣━━41.3集成模型总结（三）.mp4 [46.2M]
* ┃ ┣━━41.4集成模型总结（四）.mp4 [39M]
* ┃ ┣━━41.5集成模型总结（五）.mp4 [77.4M]
* ┃ ┣━━41.6GDBT理解及其衍生应用（一）.mp4 [39.2M]
* ┃ ┣━━41.7GDBT理解及其衍生应用（二）.mp4 [53.7M]
* ┃ ┣━━41.8GDBT理解及其衍生应用（三）.mp4 [30.9M]
* ┃ ┗━━41.9GDBT理解及其衍生应用（四）.mp4 [63M]
* ┃ ┣━━42.1SVM比较其他分类起代码（一）.mp4 [38.2M]
* ┃ ┣━━42.2SVM比较其他分类起代码（二）.mp4 [55.6M]
* ┃ ┣━━42.3网络（一）.mp4 [32M]
* ┃ ┣━━42.4网络（二）.mp4 [43M]
* ┃ ┣━━42.5网络（三）.mp4 [35.9M]
* ┃ ┗━━42.6网络（四）.mp4 [47.7M]
* ┃ ┣━━43.4机器学习工作流程（三）.mp4 [20M]
* ┃ ┣━━43.10经验分享（一）.mp4 [28.5M]
* ┃ ┣━━43.12经验分享（三）.mp4 [34.2M]
* ┃ ┣━━43.1机器学习的概念和监督学习.mp4 [21.6M]
* ┃ ┣━━43.2机器学习工作流程（一）.mp4 [11.7M]
* ┃ ┣━━43.3机器学习工作流程（二）.mp4 [21M]
* ┃ ┣━━43.11经验分享（二）.mp4 [38.6M]
* ┃ ┣━━43.5机器学习工作流程（四）.mp4 [26.7M]
* ┃ ┣━━43.6案例分析（一）.mp4 [17.1M]
* ┃ ┣━━43.7案例分析（二）.mp4 [40.8M]
* ┃ ┣━━43.8案例分析（三）.mp4 [39.3M]
* ┃ ┗━━43.9案例分析（四）.mp4 [61.6M]
* ┃ ┣━━44.3数据探索（一）.mp4 [27.1M]
* ┃ ┣━━44.10模型训练与选择（二）.mp4 [51.8M]
* ┃ ┣━━44.12Airbnb数据探索过程（二）.mp4 [59.2M]
* ┃ ┣━━44.13地震数据可视化过程（一）.mp4 [33.6M]
* ┃ ┣━━44.14地震数据可视化过程（二）.mp4 [32.6M]
* ┃ ┣━━44.1常用的分类算法.mp4 [18.6M]
* ┃ ┣━━44.2模型评估标准和案例分析.mp4 [28.8M]
* ┃ ┣━━44.11Airbnb数据探索过程（一）.mp4 [40.7M]
* ┃ ┣━━44.4数据探索（二）.mp4 [41.2M]
* ┃ ┣━━44.5数据探索（三）.mp4 [33.3M]
* ┃ ┣━━44.6数据探索（四）.mp4 [27.6M]
* ┃ ┣━━44.7数据探索（五）.mp4 [52.3M]
* ┃ ┣━━44.8数据探索（六）.mp4 [37.9M]
* ┃ ┗━━44.9模型训练与选择（一）.mp4 [34.8M]
* ┃ ┣━━45.3网络（三）.mp4 [21.7M]
* ┃ ┣━━45.10网络（十）.mp4 [40.8M]
* ┃ ┣━━45.12卷积（一）.mp4 [76.3M]
* ┃ ┣━━45.13卷积（二）.mp4 [43.9M]
* ┃ ┣━━45.1网络（一）.mp4 [38.9M]
* ┃ ┣━━45.2网络（二）.mp4 [26.8M]
* ┃ ┣━━45.11图像处理基础.mp4 [29.8M]
* ┃ ┣━━45.4网络（四）.mp4 [100.5M]
* ┃ ┣━━45.6网络（六）.mp4 [36M]
* ┃ ┣━━45.7网络（七）.mp4 [26.2M]
* ┃ ┣━━45.8【（致富网赚论坛zhifuwz.net）】网络（八）.mp4 [33.4M]
* ┃ ┣━━45.9网络（九）.mp4 [39.6M]
* ┃ ┗━━45.网络（五）.mp4 [107.9M]
* ┃ ┣━━46.3差分自回归移动平均模型(ARIMA).mp4 [31.2M]
* ┃ ┣━━46.10长短期记忆网络（LSTM）案例分析.mp4 [44.2M]
* ┃ ┣━━46.12Facebook开源的新预测工具—Prophet（二）.mp4 [50M]
* ┃ ┣━━46.13课程答疑.mp4 [43.9M]
* ┃ ┣━━46.1时间序列预测概述（一）.mp4 [21.1M]
* ┃ ┣━━46.2时间序列预测概述（二）.mp4 [25.4M]
* ┃ ┣━━46.11Facebook开源的新预测工具—Prophet（一）.mp4 [44.2M]
* ┃ ┣━━46.4差分自回归移动平均模型(ARIMA)案例分析（一）.mp4 [46.2M]
* ┃ ┣━━46.5差分自回归移动平均模型(ARIMA)案例分析（二）.mp4 [52.1M]
* ┃ ┣━━46.6差分自回归移动平均模型(ARIMA)案例分析（三）.mp4 [26.2M]
* ┃ ┣━━46.7差分自回归移动平均模型(ARIMA)案例分析（四）.mp4 [44.5M]
* ┃ ┣━━46.8长短期记忆网络（LSTM）（一）.mp4 [21.1M]
* ┃ ┗━━46.9长短期记忆网络（LSTM）（二）.mp4 [20.7M]
* ┃ ┣━━47.1人工智能金融应用（一）.mp4 [28.7M]
* ┃ ┣━━47.2人工智能金融应用（二）.mp4 [40.7M]
* ┃ ┣━━47.3人工智能金融应用（三）.mp4 [37.1M]
* ┃ ┣━━47.4人工智能金融应用（四）.mp4 [47.7M]
* ┃ ┣━━47.5机器学习方法（一）.mp4 [35.2M]
* ┃ ┣━━47.6机器学习方法（二）.mp4 [28.6M]
* ┃ ┣━━47.7机器学习方法（三）.mp4 [31.3M]
* ┃ ┗━━47.8机器学习方法（四）.mp4 [43.1M]
* ┃ ┣━━48.1计算机视觉深度学习入门概述.mp4 [103.6M]
* ┃ ┣━━48.2计算机视觉领域正在关心的问题（一）.mp4 [133.8M]
* ┃ ┣━━48.3计算机视觉领域正在关心的问题（二）.mp4 [73.8M]
* ┃ ┣━━48.4实际问题转化为具体问题并用深度学习解决（一）.mp4 [119.2M]
* ┃ ┣━━48.5实际问题转化为具体问题并用深度学习解决（二）.mp4 [153.3M]
* ┃ ┣━━48.6实际问题转化为具体问题并用深度学习解决（三）.mp4 [94.6M]
* ┃ ┗━━48.7实际问题转化为具体问题并用深度学习解决（四）.mp4 [123.4M]
* ┃ ┣━━49.3特征如何组织（二）.mp4 [86M]
* ┃ ┣━━49.10结构之间的优劣评判以及实验结果（五）.mp4 [99.6M]
* ┃ ┣━━49.12结构之间的以及实验结果（七）.mp4 [108.3M]
* ┃ ┣━━49.13结构之间的优劣评判以及实验结果（八）.mp4 [111.1M]
* ┃ ┣━━49.1复习计算机视觉最主要的负责特征提取的结构CNN.mp4 [131.9M]
* ┃ ┣━━49.2特征如何组织（一）.mp4 [126.2M]
* ┃ ┣━━49.11结构之间的优劣评判以及实验结果（六）.mp4 [77.5M]
* ┃ ┣━━49.4特征如何组织（三）.mp4 [92.9M]
* ┃ ┣━━49.5特征如何组织（四）.mp4 [113.4M]
* ┃ ┣━━49.6结构之间的优劣评判以及实验结果（一）.mp4 [116.3M]
* ┃ ┣━━49.7结构之间的优劣评判以及实验结果（二）.mp4 [87.7M]
* ┃ ┣━━49.8结构之间的优劣评判以及实验结果（三）.mp4 [148.2M]
* ┃ ┗━━49.9结构之间的优劣评判以及实验结果（四）.mp4 [91.3M]
* ┃ ┣━━50.1计算机视觉学习入门：优化篇概述.mp4 [70.4M]
* ┃ ┣━━50.2CNN模型的一阶优化逻辑.mp4 [148.7M]
* ┃ ┣━━50.3稳定性：Annealing和Momentum.mp4 [69.5M]
* ┃ ┣━━50.4拟合：从Dropout到Weight Decay.mp4 [128M]
* ┃ ┣━━50.5优化器和多机并行.mp4 [134.9M]
* ┃ ┗━━50.6手动超参优化逻辑以及超参优化往何处去.mp4 [124M]
* ┃ ┣━━51.1计算机视觉领域的常用竞赛数据集.mp4 [104.3M]
* ┃ ┣━━51.2对数据常用的预处理工作和后处理工作如何提高竞赛成绩（一）.mp4 [104.7M]
* ┃ ┣━━51.3对数据常用的预处理工作和后处理工作如何提高竞赛成绩（二）.mp4 [81.4M]
* ┃ ┗━━51.4如何使用端到端深度学习的方法.mp4 [139.3M]
* ┃ ┣━━52.1计算机视觉深度学习入门工具篇（一）.mp4 [93.5M]
* ┃ ┣━━52.2计算机视觉深度学习入门工具篇（二）.mp4 [110.2M]
* ┃ ┗━━52.3计算机视觉深度学习入门工具篇（三）.mp4 [53.2M]
* ┃ ┣━━53.10工程望.mp4 [33.7M]
* ┃ ┣━━53.1个化推荐的发展.mp4 [26.1M]
* ┃ ┣━━53.2推荐算法的演进（一）.mp4 [25.5M]
* ┃ ┣━━53.3推荐算法的演进（二）.mp4 [35.8M]
* ┃ ┣━━53.4推荐算法的演进（三）.mp4 [27.9M]
* ┃ ┣━━53.5推荐算法的演进（四）.mp4 [40.2M]
* ┃ ┣━━53.6建模step by step（一）.mp4 [34M]
* ┃ ┣━━53.7建模step by step（二）.mp4 [38.7M]
* ┃ ┣━━53.8建模step by step（三）.mp4 [32.7M]
* ┃ ┗━━53.9算法评估和迭代.mp4 [18.9M]
* ┃ ┣━━54.10Spark巩固（五）.mp4 [101.7M]
* ┃ ┣━━54.1Pig巩固（一）.mp4 [43.8M]
* ┃ ┣━━54.2Pig巩固（二）.mp4 [115.3M]
* ┃ ┣━━54.3Pig巩固（三）.mp4 [89.1M]
* ┃ ┣━━54.4Pig巩固（四）.mp4 [83M]
* ┃ ┣━━54.5Pig巩固（五）.mp4 [70.1M]
* ┃ ┣━━54.6Spark巩固（一）.mp4 [65.6M]
* ┃ ┣━━54.7Spark巩固（二）.mp4 [105.4M]
* ┃ ┣━━54.8Spark巩固（三）.mp4 [70.6M]
* ┃ ┗━━54.9Spark巩固（四）.mp4 [54.9M]
* ┃ ┣━━55.10使用人工智能的方式.mp4 [26.1M]
* ┃ ┣━━55.1智能存在的意义是什么.mp4 [19.5M]
* ┃ ┣━━55.2已有人工智的设计应用.mp4 [18.6M]
* ┃ ┣━━55.3人的智能（一）.mp4 [17M]
* ┃ ┣━━55.4人的智能（二）.mp4 [28.7M]
* ┃ ┣━━55.5人的智能的特点（一）.mp4 [29.6M]
* ┃ ┣━━55.6人的智能的特点（二）.mp4 [27.9M]
* ┃ ┣━━55.7人的智能的特点（三）.mp4 [41.6M]
* ┃ ┣━━55.8人工智能（一）.mp4 [27.6M]
* ┃ ┗━━55.9人工智能（二）.mp4 [24.6M]
* ┃ ┣━━56.1卷积的本质.mp4 [27.8M]
* ┃ ┣━━56.2卷积的三大特点.mp4 [34.1M]
* ┃ ┣━━56.3Pooling.mp4 [17M]
* ┃ ┣━━56.4数字识别（一）.mp4 [33.2M]
* ┃ ┣━━56.5数字识别（二）.mp4 [32M]
* ┃ ┣━━56.6感受野.mp4 [23.3M]
* ┃ ┗━━56.7RNN.mp4 [23.5M]
* ┃ ┣━━57.1非线动力学.mp4 [23.1M]
* ┃ ┣━━57.2线动力系统.mp4 [40M]
* ┃ ┣━━57.3线动力学与非线动力学系统（一）.mp4 [40.4M]
* ┃ ┣━━57.4线动力学与非线动力学系统（二）.mp4 [39.8M]
* ┃ ┗━━57.6Poincare引理.mp4 [37.1M]
* ┃ ┣━━58.1交易.mp4 [20.2M]
* ┃ ┣━━58.2点过程基础（一）.mp4 [13.5M]
* ┃ ┣━━58.3点过程基础（二）.mp4 [24.2M]
* ┃ ┣━━58.4点过程基础（三）.mp4 [17.9M]
* ┃ ┣━━58.5订单流数据分析（一）.mp4 [22.3M]
* ┃ ┣━━58.6订单流数据分析（二）.mp4 [20.9M]
* ┃ ┣━━58.7订单流数据分析（三）.mp4 [17.7M]
* ┃ ┣━━58.8订单流数据分析（四）.mp4 [20.6M]
* ┃ ┗━━58.9订单流数据分析（五）.mp4 [26.2M]
* ┃ ┣━━59.1比特币（一）.mp4 [23M]
* ┃ ┣━━59.2比特币（二）.mp4 [15.7M]
* ┃ ┣━━59.3比特币（三）.mp4 [32.1M]
* ┃ ┗━━59.4以太坊简介及ICO.mp4 [15.2M]
* ┃ ┣━━60.3抛硬币抛出正态分布（一）.mp4 [19.5M]
* ┃ ┣━━60.10证明理想气体方程.mp4 [23.3M]
* ┃ ┣━━60.12四大热力学势（一）.mp4 [30.1M]
* ┃ ┣━━60.13 四大热力学势（二）.mp4 [38.4M]
* ┃ ┣━━60.1统计物理的开端(一).mp4 [33M]
* ┃ ┣━━60.2统计物理的开端（二）.mp4 [24.2M]
* ┃ ┣━━60.11化学势.mp4 [41.5M]
* ┃ ┣━━60.4抛硬币抛出正态分布（二）.mp4 [35.7M]
* ┃ ┣━━60.5再造整个世界（一）.mp4 [30.5M]
* ┃ ┣━━60.6再造整个世界（二）.mp4 [35.1M]
* ┃ ┣━━60.7温度的本质（一）.mp4 [41M]
* ┃ ┣━━60.8温度的本质（二）.mp4 [27.3M]
* ┃ ┗━━60.9.mp4 [33.6M]
* ┃ ┣━━61.1神奇公式.mp4.mp4 [34.7M]
* ┃ ┣━━61.2信息熵（一）.mp4 [17.9M]
* ┃ ┣━━61.3信息熵（二）.mp4 [28.6M]
* ┃ ┣━━61.4Boltzmann分布.mp4 [30.2M]
* ┃ ┗━━61.5配分函数Z.mp4 [38.8M]
* ┃ ┣━━62.1Networks in real worlds.mp4 [14.3M]
* ┃ ┣━━62.2BasicConcepts（一）.mp4 [19.8M]
* ┃ ┣━━62.3BasicConcepts（二）.mp4 [13.3M]
* ┃ ┣━━62.4Models（一）.mp4 [12.1M]
* ┃ ┣━━62.5Models（二）.mp4 [14M]
* ┃ ┣━━62.6Algorithms（一）.mp4 [25.4M]
* ┃ ┗━━62.7Algorithms（二）.mp4 [34.7M]
* ┃ ┣━━63.1课程介绍.mp4 [26.9M]
* ┃ ┣━━63.10ABM与复杂系统建模-交通系统（一）.mp4 [27.2M]
* ┃ ┣━━63.12ABM金融市场-SFI股票市场模型（一）.mp4 [32M]
* ┃ ┣━━63.13ABM金融市场-SFI股票市场模型（二）.mp4 [25.4M]
* ┃ ┣━━63.14ABM金融市场-genova市场模型.mp4 [31.9M]
* ┃ ┣━━63.15ABM金融市场-Agent及其行为.mp4 [31.8M]
* ┃ ┣━━63.16学习模型.mp4 [35.7M]
* ┃ ┣━━63.17ABM金融市场-价格形成机制.mp4 [15.8M]
* ┃ ┣━━63.18ABM的特点.mp4 [29.3M]
* ┃ ┣━━63.11ABM与复杂系统建模-交通系统（二）.mp4 [37.9M]
* ┃ ┣━━63.2系统与系统建模.mp4 [39.5M]
* ┃ ┣━━63.3ABM与复杂系统建模（一）.mp4 [36.3M]
* ┃ ┣━━63.4ABM与复杂系统建模（二）.mp4 [45.1M]
* ┃ ┣━━63.5ABM与复杂系统建模（三）.mp4 [36.8M]
* ┃ ┣━━63.6ABM为经济系统建模.mp4 [30.4M]
* ┃ ┣━━63.7经典经济学如何给市场建模.mp4 [35.3M]
* ┃ ┣━━63.8ABM与复杂系统建模-市场交易.mp4 [40.1M]
* ┃ ┗━━63.9ABM与复杂系统建模-技术扩散.mp4 [25.3M]
* ┃ ┣━━64.19答疑.mp4 [17.4M]
* ┃ ┣━━64.10（网络中的）投票模型.mp4 [24.2M]
* ┃ ┣━━64.12集体运动Vicsek模型.mp4 [38.3M]
* ┃ ┣━━64.13自旋玻璃.mp4 [18.2M]
* ┃ ┣━━64.14Hopfield神经网络.mp4 [23.3M]
* ┃ ┣━━64.15限制Boltzmann机.mp4 [30.2M]
* ┃ ┣━━64.16深度学习与重正化群（一）.mp4 [35.4M]
* ┃ ┣━━64.17深度学习与重正化群（二）.mp4 [21.9M]
* ┃ ┣━━64.18总结.mp4 [30.7M]
* ┃ ┣━━64.11观念动力学.mp4 [29.8M]
* ┃ ┣━━64.1伊辛模型的背景及格气模型.mp4 [24.4M]
* ┃ ┣━━64.2伊辛模型（一）.mp4 [19.2M]
* ┃ ┣━━64.3伊辛模型（二）.mp4 [19.6M]
* ┃ ┣━━64.4从能量到统计分布及Monte Carlo模拟.mp4 [23.6M]
* ┃ ┣━━64.5Ising Model(2D).mp4 [26M]
* ┃ ┣━━64.6相变和临界现象.mp4 [43.6M]
* ┃ ┣━━64.7Critical Exponents.mp4 [26.7M]
* ┃ ┣━━64.8正问题和反问题.mp4 [29.1M]
* ┃ ┗━━64.9（空间中的）投票模型.mp4 [36.4M]
* ┃ ┣━━65.19总结.mp4 [21.1M]
* ┃ ┣━━65.10Classical Benchmarks（五）.mp4 [29.7M]
* ┃ ┣━━65.12Endogenous Risk（二）.mp4 [36.8M]
* ┃ ┣━━65.13Endogenous Risk（三）.mp4 [40.2M]
* ┃ ┣━━65.14Endogenous Risk（四）.mp4 [18.3M]
* ┃ ┣━━65.15Endogenous Risk（五）.mp4 [35.5M]
* ┃ ┣━━65.16Endogenous Risk（六）.mp4 [37.1M]
* ┃ ┣━━65.17Heterogeneous Beliefs（一）.mp4 [42.4M]
* ┃ ┣━━65.18Heterogeneous Beliefs（二）.mp4 [44.8M]
* ┃ ┣━━65.11Endogenous Risk（一）.mp4 [42.8M]
* ┃ ┣━━65.1导论（一）.mp4 [39M]
* ┃ ┣━━65.2导论（二）.mp4 [39.7M]
* ┃ ┣━━65.3导论（三）.mp4 [21.4M]
* ┃ ┣━━65.4导论（四）.mp4 [30.3M]
* ┃ ┣━━65.5导论（五）.mp4 [37.6M]
* ┃ ┣━━65.6Classical Benchmarks（一）.mp4 [31.9M]
* ┃ ┣━━65.7Classical Benchmarks（二）.mp4 [28.6M]
* ┃ ┣━━65.8Classical Benchmarks（三）.mp4 [40M]
* ┃ ┗━━65.9Classical Benchmarks（四）.mp4 [22.4M]
* ┃ ┣━━66.1界（一）.mp4 [29M]
* ┃ ┣━━66.2界（二）.mp4 [24.3M]
* ┃ ┣━━66.3界（三）.mp4 [22.9M]
* ┃ ┣━━66.4界（四）.mp4 [31M]
* ┃ ┣━━66.5城市、商业（一）.mp4 [33.9M]
* ┃ ┣━━66.6城市、商业（二）.mp4 [33.3M]
* ┃ ┣━━66.7启示（一）.mp4 [31.5M]
* ┃ ┣━━66.8启示（二）.mp4 [17.6M]
* ┃ ┗━━66.9总结.mp4 [18.3M]
* ┃ ┣━━67.2概括（一）.mp4 [29.2M]
* ┃ ┣━━67.10粒子群算法（一）.mp4 [37.2M]
* ┃ ┣━━67.12粒子群算法（三）.mp4 [33.6M]
* ┃ ┣━━67.13遗传算法和PSO的比较.mp4 [25.7M]
* ┃ ┣━━67.14更多的类似的算法（一）.mp4 [34.9M]
* ┃ ┣━━67.15更多的类似的算法（二）.mp4 [27.4M]
* ┃ ┣━━67.16答疑.mp4 [35.4M]
* ┃ ┣━━67.1课程回顾及答疑.mp4 [29.7M]
* ┃ ┣━━67.11粒子群算法（二）.mp4 [38.2M]
* ┃ ┣━━67.3概括（二）.mp4 [15.9M]
* ┃ ┣━━67.4模拟退火算法（一）.mp4 [40.2M]
* ┃ ┣━━67.5模拟退火算法（二）.mp4 [32.8M]
* ┃ ┣━━67.6进化相关的算法（一）.mp4 [26.4M]
* ┃ ┣━━67.7进化相关的算法（二）.mp4 [29.7M]
* ┃ ┣━━67.8进化相关的算法（三）.mp4 [36M]
* ┃ ┗━━67.9进化相关的算法（四）.mp4 [27.6M]
* ┃ ┣━━68.2阅读论文.mp4 [19.9M]
* ┃ ┣━━68.10输出是最好的学习（二）.mp4 [16M]
* ┃ ┣━━68.12案例（二）.mp4 [18.3M]
* ┃ ┣━━68.13案例（三）.mp4 [20.5M]
* ┃ ┣━━68.14案例（四）.mp4 [38M]
* ┃ ┣━━68.15案例（五）.mp4 [16.4M]
* ┃ ┣━━68.1为什么要讲学习方法.mp4 [24.7M]
* ┃ ┣━━68.11案例（一）.mp4 [27.6M]
* ┃ ┣━━68.3综述式文章举例（一）.mp4 [88.3M]
* ┃ ┣━━68.4综述式文章举例（二）.mp4 [150.5M]
* ┃ ┣━━68.5碎片化时间学习及书籍.mp4 [51.6M]
* ┃ ┣━━68.6视频学习资源及做思维导图.mp4 [33.8M]
* ┃ ┣━━68.7铁哥答疑（一）.mp4 [28.5M]
* ┃ ┣━━68.8铁哥答疑（二）.mp4 [19.2M]
* ┃ ┗━━68.9输出是最好的学习（一）.mp4 [22.2M]
* ┃ ┣━━69.1课程简介.mp4 [20.7M]
* ┃ ┣━━69.10定制化可视化系统—Jupyter Dashboard（一）.mp4 [30.8M]
* ┃ ┣━━69.12变身前端—seaboarn+Bokeh+Echarts.mp4 [71.3M]
* ┃ ┣━━69.13日志管理系统—ELK.mp4 [50.4M]
* ┃ ┣━━69.14极速Bi系统—superset.mp4 [40.2M]
* ┃ ┣━━69.15Dashboard补充.mp4 [55.4M]
* ┃ ┣━━69.16ELK补充.mp4 [63.3M]
* ┃ ┣━━69.17Superset补充.mp4 [60.9M]
* ┃ ┣━━69.18Superset补充及总结.mp4 [20.4M]
* ┃ ┣━━69.11定制化可视化系统—Jupyter Dashboard（二）.mp4 [38.7M]
* ┃ ┣━━69.2虚拟换环境—Anaconda&docker（一）.mp4 [28.7M]
* ┃ ┣━━69.3虚拟换环境—Anaconda&docker（二）.mp4 [30M]
* ┃ ┣━━69.4虚拟换环境—Anaconda&docker（三）.mp4 [59.3M]
* ┃ ┣━━69.5虚拟换环境—Anaconda&docker（四）.mp4 [34.5M]
* ┃ ┣━━69.6虚拟换环境—Anaconda&docker（五）.mp4 [53.3M]
* ┃ ┣━━69.7虚拟换环境—Anaconda&docker（六）.mp4 [38.2M]
* ┃ ┣━━69.8虚拟换环境—Anaconda&docker（七）.mp4 [54.6M]
* ┃ ┗━━69.9虚拟换环境—Anaconda&docker（八）.mp4 [138.5M]
* ┃ ┣━━70.1Background&Motivation.mp4 [23M]
* ┃ ┣━━70.2Value Iteration.mp4 [36.7M]
* ┃ ┣━━70.3Grid—world Domain.mp4 [23.8M]
* ┃ ┗━━70.4总结及答疑.mp4 [26.4M]
* ┃ ┣━━0822 CNN RNN回顾 非线性动力学引入.mp4 [469.9M]
* ┃ ┗━━0822 高频订单流模型、区块链介绍.mp4 [371.7M]
* ┃ ┣━━71.1线动力学系统（一）.mp4 [28M]
* ┃ ┣━━71.10混沌（一）.mp4 [28.6M]
* ┃ ┣━━71.12混沌（三）.mp4 [21.4M]
* ┃ ┣━━71.13混沌（四）.mp4 [24M]
* ┃ ┣━━71.14混沌（五）.mp4 [32.4M]
* ┃ ┣━━71.15混沌（六）.mp4 [86.2M]
* ┃ ┣━━71.16混沌（七）.mp4 [157.7M]
* ┃ ┣━━71.17混沌（八）.mp4 [31.5M]
* ┃ ┣━━71.18混沌（九）.mp4 [31.5M]
* ┃ ┣━━71.19混沌（十）.mp4 [19.8M]
* ┃ ┣━━71.11混沌（二）.mp4 [24.6M]
* ┃ ┣━━71.20混沌（十一）.mp4 [125.6M]
* ┃ ┣━━71.2线动力学系统（二）.mp4 [33.7M]
* ┃ ┣━━71.3二维系统动力学综述—Poincare引理.mp4 [33.7M]
* ┃ ┣━━71.4Bifurcation（一）.mp4 [13.7M]
* ┃ ┣━━71.5Bifurcation（二）.mp4 [34.2M]
* ┃ ┣━━71.6Bifurcation（三）.mp4 [32M]
* ┃ ┣━━71.7Bifurcation（四）.mp4 [28.7M]
* ┃ ┣━━71.8Bifurcation（五）.mp4 [38M]
* ┃ ┗━━71.9Bifurcation（六）.mp4 [65.6M]
* ┃ ┣━━72.1自然语言处理（一）.mp4 [30.9M]
* ┃ ┣━━72.2自然语言处理（二）.mp4 [34.1M]
* ┃ ┣━━72.3RNN.mp4 [34.4M]
* ┃ ┗━━72.4RNN及.mp4 [30.8M]
* ┃ ┣━━73.1中文分词.mp4 [27.7M]
* ┃ ┣━━73.2中文分词、依存文法分析.mp4 [26.7M]
* ┃ ┣━━73.3篇章分析、自动摘要、知识提取、文本相似度计算.mp4 [40.9M]
* ┃ ┣━━73.4知识库构建、问答系统.mp4 [42.7M]
* ┃ ┣━━73.5示范2的豆瓣评论词云（一）.mp4 [56.9M]
* ┃ ┣━━73.6示范2的豆瓣评论词云（二）.mp4 [54.8M]
* ┃ ┣━━73.7示范2的豆瓣评论词云（三）.mp4 [62.7M]
* ┃ ┣━━73.8示范2的豆瓣评论词云（四）.mp4 [72.2M]
* ┃ ┗━━73.9示范2的豆瓣评论词云（五）.mp4 [62.9M]
* ┃ ┣━━74.2常用的统计描述物理量.mp4 [14.4M]
* ┃ ┣━━74.10一些传播动力学模型（七）.mp4 [32.4M]
* ┃ ┣━━74.12仿真模型的建立过程（一）.mp4 [45M]
* ┃ ┣━━74.13仿真模型的建立过程（二）.mp4 [44.3M]
* ┃ ┣━━74.14仿真模型的建立过程（三）.mp4 [63.7M]
* ┃ ┣━━74.15仿真模型的建立过程（四）.mp4 [44.2M]
* ┃ ┣━━74.16Combining complex networks and data mining.mp4 [31.9M]
* ┃ ┣━━74.1一些基本概念.mp4 [18.5M]
* ┃ ┣━━74.11一些传播动力学模型（八）.mp4 [23.5M]
* ┃ ┣━━74.3四种网络模型.mp4 [30.3M]
* ┃ ┣━━74.4一些传播动力学模型（一）.mp4 [28.2M]
* ┃ ┣━━74.5一些传播动力学模型（二）.mp4 [28.9M]
* ┃ ┣━━74.6一些传播动力学模型（三）.mp4 [29.6M]
* ┃ ┣━━74.7一些传播动力学模型（四）.mp4 [31.4M]
* ┃ ┣━━74.8一些传播动力学模型（五）.mp4 [29.5M]
* ┃ ┗━━74.9一些传播动力学模型（六）.mp4 [27.8M]
* ┃ ┣━━75.1RNN—序列处理器（一）.mp4 [23.3M]
* ┃ ┣━━75.10梯度消失与梯度爆炸（二）.mp4 [23.9M]
* ┃ ┣━━75.12LSTM.mp4 [32.5M]
* ┃ ┣━━75.13LSTM、Use Examples.mp4 [36.5M]
* ┃ ┣━━75.14词向量、Deep RNN.mp4 [22M]
* ┃ ┣━━75.15Encoder Decoder Structure.mp4 [20.2M]
* ┃ ┣━━75.16LSTM Text Generation（一）.mp4 [44.1M]
* ┃ ┣━━75.17LSTM Text Generation（二）.mp4 [53.5M]
* ┃ ┣━━75.18LSTM Text Generation（三）.mp4 [54.8M]
* ┃ ┣━━75.11Reservoir computing—偷懒方法.mp4 [19.8M]
* ┃ ┣━━75.2RNN—序列处理器（二）.mp4 [31.4M]
* ┃ ┣━━75.3A simple enough case.mp4 [29M]
* ┃ ┣━━75.4A dance between fix points.mp4 [30.7M]
* ┃ ┣━━75.5Fix point、Train Chaos.mp4 [26.8M]
* ┃ ┣━━75.6RNN作为生成模型（动力系统）.mp4 [24.6M]
* ┃ ┣━━75.7RNN训练—BPTT（一）.mp4 [22.9M]
* ┃ ┣━━75.8RNN训练—BPTT（二）.mp4 [18.8M]
* ┃ ┗━━75.9梯度消失与梯度爆炸（一）.mp4 [22.7M]
* ┃ ┣━━76.1人工智能对我们生活的影响（一）.mp4 [51.7M]
* ┃ ┣━━76.10三个战略管理学商业模型（三）.mp4 [39.7M]
* ┃ ┣━━76.12三个战略管理学商业模型（五）.mp4 [31.4M]
* ┃ ┣━━76.13三个战略管理学商业模型（六）.mp4 [96.1M]
* ┃ ┣━━76.14三个战略管理学商业模型（七）.mp4 [33.2M]
* ┃ ┣━━76.15三个战略管理学商业模型（八）.mp4 [22.9M]
* ┃ ┣━━76.16三个战略管理学商业模型（九）.mp4 [27M]
* ┃ ┣━━76.17关于Entrepreneurship.mp4 [13.3M]
* ┃ ┣━━76.11三个战略管理学商业模型（四）.mp4 [41.5M]
* ┃ ┣━━76.2人工智能对我们生活的影响（二）.mp4 [38.1M]
* ┃ ┣━━76.3人工智能对我们生活的影响（三）.mp4 [47.4M]
* ┃ ┣━━76.4人工智能对我们生活的影响（四）.mp4 [67.9M]
* ┃ ┣━━76.5人工智能对我们生活的影响（五）.mp4 [70.7M]
* ┃ ┣━━76.6人工智能对我们生活的影响（六）.mp4 [105.5M]
* ┃ ┣━━76.7人工智能创业中的商业思维.mp4 [29.7M]
* ┃ ┣━━76.8三个战略管理学商业模型（一）.mp4 [19.8M]
* ┃ ┗━━76.9三个战略管理学商业模型（二）.mp4 [19.4M]
* ┃ ┣━━77.1.mp4 [30.3M]
* ┃ ┣━━77.10程序讲解（三）.mp4 [47.9M]
* ┃ ┣━━77.2玻尔兹曼机—联想的机器.mp4 [27.7M]
* ┃ ┣━━77.3玻尔兹曼机.mp4 [36.2M]
* ┃ ┣━━77.4学习（一）.mp4 [26.4M]
* ┃ ┣━━77.5学习（二）.mp4 [24.2M]
* ┃ ┣━━77.6学习（三）.mp4 [24.4M]
* ┃ ┣━━77.7学习（四）.mp4 [54.5M]
* ┃ ┣━━77.8程序讲解（一）.mp4 [43M]
* ┃ ┗━━77.9程序讲解（二）.mp4 [54.5M]
* ┣━━78.10课程总结（二）.mp4 [132.7M]
* ┣━━78.1开场.mp4 [21.2M]
* ┣━━78.2Attention实例—Spatial Transformer.mp4 [60.9M]
* ┣━━78.3猫狗大战—CNN实战（一）.mp4 [42M]
* ┣━━78.4猫狗大战—CNN实战（二）.mp4 [35.4M]
* ┣━━78.5RNN诗人.mp4 [28.9M]
* ┣━━78.6课程复习.mp4 [33.2M]
* ┣━━78.7课程大纲（一）.mp4 [18.4M]
* ┣━━78.8课程大纲（二）.mp4 [18.8M]
* ┗━━78.9课程总结（一）.mp4 [16.5M]
