【尊享】ZX023 – Python机器学习实训营（2020版） [7.9G]

【尊享】ZX023 – Python机器学习实训营（2020版） [7.9G]

┣━━01.视频 [6.9G]
┃ ┣━━01.Python机器学习实训营课程介绍_.vep [22.8M]
┃ ┣━━02.回归问题概述_.vep [26M]
┃ ┣━━03.误差项定义_.vep [21.2M]
┃ ┣━━04.独立同分布的意义_.vep [18.5M]
┃ ┣━━05.似然函数的作用_.vep [24.8M]
┃ ┣━━06.参数求解_.vep [26.3M]
┃ ┣━━07.梯度下降通俗解释_.vep [17M]
┃ ┣━━08.参数更新方法_.vep [20.5M]
┃ ┣━━09.优化参数设置_.vep [21M]
┃ ┣━━10.是代码资料见资料文件夹.txt [0B]
┃ ┣━━11.线性回归整体模块概述_.vep [21M]
┃ ┣━━12.初始化步骤_.vep [18.2M]
┃ ┣━━13.实现梯度下降优化模块_.vep [31.1M]
┃ ┣━━14.损失与预测模块_.vep [35.5M]
┃ ┣━━15.数据与标签定义_.vep [35M]
┃ ┣━━16.训练线性回归模型_.vep [78.1M]
┃ ┣━━17.得到线性回归方程_.vep [66.6M]
┃ ┣━━18.整体流程debug解读_.vep [26.7M]
┃ ┣━━19.多特征回归模型_.vep [41.7M]
┃ ┣━━20.非线性回归_.vep [37.4M]
┃ ┣━━21.Sklearn工具包简介_.vep [20.4M]
┃ ┣━━22.数据集切分_.vep [20.2M]
┃ ┣━━23.交叉验证的作用_.vep [30.6M]
┃ ┣━━24.交叉验证实验分析_.vep [45.6M]
┃ ┣━━25.混淆矩阵_.vep [19M]
┃ ┣━━26.评估指标对比分析_.vep [38.1M]
┃ ┣━━27.阈值对结果的影响_.vep [27.3M]
┃ ┣━━28.ROC曲线_.vep [23M]
┃ ┣━━29.实验目标分析_.vep [14.2M]
┃ ┣━━30.参数直接求解方法_.vep [40.5M]
┃ ┣━━31.预处理对结果的影响_.vep [34.2M]
┃ ┣━━32.梯度下降模块_.vep [34.1M]
┃ ┣━━33.学习率对结果的影响_.vep [23.9M]
┃ ┣━━34.随机梯度下降得到的效果_.vep [30.4M]
┃ ┣━━35.MiniBatch方法_.vep [21.1M]
┃ ┣━━36.不同策略效果对比_.vep [23.1M]
┃ ┣━━37.多项式回归_.vep [27.6M]
┃ ┣━━38.模型复杂度_.vep [43.5M]
┃ ┣━━39.样本数量对结果的影响_.vep [43.3M]
┃ ┣━━40.正则化的作用_.vep [23.3M]
┃ ┣━━41.岭回归与lasso_.vep [59.2M]
┃ ┣━━42.实验总结_.vep [39.2M]
┃ ┣━━43.逻辑回归算法原理_.vep [27.8M]
┃ ┣━━44.化简与求解_.vep [22.5M]
┃ ┣━━45.多分类逻辑回归整体思路_.vep [16.6M]
┃ ┣━━46.训练模块功能_.vep [32.2M]
┃ ┣━━47.完成预测模块_.vep [26.5M]
┃ ┣━━48.优化目标定义_.vep [28.4M]
┃ ┣━━49.迭代优化参数_.vep [34.1M]
┃ ┣━━50.梯度计算_.vep [79.6M]
┃ ┣━━51.得出最终结果_.vep [39.4M]
┃ ┣━━52.鸢尾花数据集多分类任务_.vep [46.4M]
┃ ┣━━53.训练多分类模型_.vep [86.6M]
┃ ┣━━54.准备测试数据_.vep [31.9M]
┃ ┣━━55.决策边界绘制_.vep [118.3M]
┃ ┣━━56.非线性决策边界_.vep [17.1M]
┃ ┣━━57.逻辑回归实验概述_.vep [33.2M]
┃ ┣━━58.概率结果随特征数值的变化_.vep [26.5M]
┃ ┣━━59.可视化展示_.vep [23.9M]
┃ ┣━━60.坐标棋盘制作_.vep [26.3M]
┃ ┣━━61.分类决策边界展示分析_.vep [39M]
┃ ┣━━62.多分类softmax_.vep [133.7M]
┃ ┣━━63.KMEANS算法概述_.vep [18.9M]
┃ ┣━━64.KMEANS工作流程_.vep [14.9M]
┃ ┣━━65.KMEANS迭代可视化展示_.vep [21.6M]
┃ ┣━━66.DBSCAN聚类算法_.vep [27M]
┃ ┣━━67.DBSCAN工作流程_.vep [26.2M]
┃ ┣━━68.DBSCAN可视化展示_.vep [22.5M]
┃ ┣━━69.Kmeans算法模块概述_.vep [6.8M]
┃ ┣━━70.计算得到簇中心点_.vep [18.8M]
┃ ┣━━71.样本点归属划分_.vep [19.5M]
┃ ┣━━72.算法迭代更新_.vep [46.8M]
┃ ┣━━73.鸢尾花数据集聚类任务_.vep [24.4M]
┃ ┣━━74.聚类效果展示_.vep [98.6M]
┃ ┣━━75.Kmenas算法常用操作_.vep [28.5M]
┃ ┣━━76.聚类结果展示_.vep [13.5M]
┃ ┣━━77.建模流程解读_.vep [32.2M]
┃ ┣━━78.不稳定结果_.vep [12.3M]
┃ ┣━━79.评估指标Inertia_.vep [28.2M]
┃ ┣━━80.如何找到合适的K值_.vep [20.8M]
┃ ┣━━81.轮廓系数的作用_.vep [29.4M]
┃ ┣━━82.Kmenas算法存在的问题_.vep [22M]
┃ ┣━━83.应用实例图像分割_.vep [31.5M]
┃ ┣━━84.半监督学习_.vep [34.4M]
┃ ┣━━85.DBSCAN算法_.vep [122.3M]
┃ ┣━━86.决策树算法概述_.vep [34.1M]
┃ ┣━━87.熵的作用_.vep [16.5M]
┃ ┣━━88.信息增益原理_.vep [39.8M]
┃ ┣━━89.决策树构造实例_.vep [35.8M]
┃ ┣━━90.信息增益率与gini系数_.vep [14.1M]
┃ ┣━━91.预剪枝方法_.vep [19.2M]
┃ ┣━━92.后剪枝方法_.vep [35.5M]
┃ ┣━━93.回归问题解决_.vep [14M]
┃ ┣━━94.整体模块概述_.vep [9.3M]
┃ ┣━━95.递归生成树节点_.vep [21.6M]
┃ ┣━━96.整体框架逻辑_.vep [15.5M]
┃ ┣━━97.熵值计算_.vep [63.9M]
┃ ┣━━98.数据集切分_.vep [20.5M]
┃ ┣━━99.完成树模型构建_.vep [21.7M]
┃ ┣━━100.测试算法效果_.vep [17.1M]
┃ ┣━━101.树模型可视化展示_.vep [21.8M]
┃ ┣━━102.决策边界展示分析_.vep [28.7M]
┃ ┣━━103.树模型预剪枝参数作用_.vep [30M]
┃ ┣━━104.回归树模型_.vep [29.8M]
┃ ┣━━105.随机森林算法原理_.vep [21.8M]
┃ ┣━━106.随机森林优势与特征重要性指标_.vep [21.2M]
┃ ┣━━107.提升算法概述_.vep [18.5M]
┃ ┣━━108.stacking堆叠模型_.vep [15.6M]
┃ ┣━━109.构建实验数据集_.vep [13.6M]
┃ ┣━━110.硬投票与软投票效果对比_.vep [46M]
┃ ┣━━111.Bagging策略效果_.vep [28.7M]
┃ ┣━━112.集成效果展示分析_.vep [105.8M]
┃ ┣━━113.OOB袋外数据的作用_.vep [11.9M]
┃ ┣━━114.特征重要性热度图展示_.vep [93.7M]
┃ ┣━━115.Adaboost算法概述_.vep [9.6M]
┃ ┣━━116.Adaboost决策边界效果_.vep [39.8M]
┃ ┣━━117.GBDT提升算法流程_.vep [38.7M]
┃ ┣━━118.集成参数对比分析_.vep [55.6M]
┃ ┣━━119.模型提前停止策略_.vep [21.2M]
┃ ┣━━120.停止方案实施_.vep [34.1M]
┃ ┣━━121.堆叠模型_.vep [42.8M]
┃ ┣━━122.支持向量机要解决的问题_.vep [26.1M]
┃ ┣━━123.距离与数据定义_.vep [26.8M]
┃ ┣━━124.目标函数推导_.vep [20.1M]
┃ ┣━━125.拉格朗日乘子法求解_.vep [17.1M]
┃ ┣━━126.化简最终目标函数_.vep [14M]
┃ ┣━━127.求解决策方程_.vep [26.6M]
┃ ┣━━128.软间隔优化_.vep [47.9M]
┃ ┣━━129.核函数的作用_.vep [26.2M]
┃ ┣━━130.知识点总结_.vep [19.2M]
┃ ┣━━131.支持向量机所能带来的效果_.vep [49.7M]
┃ ┣━━132.决策边界可视化展示_.vep [26.3M]
┃ ┣━━133.软间隔的作用_.vep [25.2M]
┃ ┣━━134.非线性SVM_.vep [17.1M]
┃ ┣━━135.核函数的作用与效果_.vep [46M]
┃ ┣━━136.深度学习要解决的问题_.vep [16.6M]
┃ ┣━━137.深度学习应用领域_.vep [97.5M]
┃ ┣━━138.计算机视觉任务_.vep [28.9M]
┃ ┣━━139.视觉任务中遇到的问题_.vep [25.3M]
┃ ┣━━140.得分函数_.vep [15.1M]
┃ ┣━━141.损失函数的作用_.vep [25.6M]
┃ ┣━━142.前向传播整体流程_.vep [32.6M]
┃ ┣━━143.返向传播计算方法_.vep [20.3M]
┃ ┣━━144.神经网络整体架构_.vep [24M]
┃ ┣━━145.神经网络架构细节_.vep [29M]
┃ ┣━━146.神经元个数对结果的影响_.vep [150.6M]
┃ ┣━━147.正则化与激活函数_.vep [42.9M]
┃ ┣━━148.神经网络过拟合解决方法_.vep [32.3M]
┃ ┣━━149.神经网络整体框架概述_.vep [16.8M]
┃ ┣━━150.参数初始化操作_.vep [34.4M]
┃ ┣━━151.矩阵向量转换_.vep [24.3M]
┃ ┣━━152.向量反变换_.vep [25.9M]
┃ ┣━━153.完成前向传播模块_.vep [28.4M]
┃ ┣━━154.损失函数定义_.vep [58.1M]
┃ ┣━━155.准备反向传播迭代_.vep [24.1M]
┃ ┣━━156.差异项计算_.vep [65.4M]
┃ ┣━━157.逐层计算_.vep [28.9M]
┃ ┣━━158.完成全部迭代更新模块_.vep [49M]
┃ ┣━━159.手写字体识别数据集_.vep [29.3M]
┃ ┣━━160.算法代码错误修正_.vep [40M]
┃ ┣━━161.测试效果可视化展示_.vep [126.2M]
┃ ┣━━162.模型优化结果展示_.vep [39.6M]
┃ ┣━━163.贝叶斯要解决的问题_.vep [11.6M]
┃ ┣━━164.贝叶斯公式推导_.vep [16.4M]
┃ ┣━━165.拼写纠错实例_.vep [26.6M]
┃ ┣━━166.垃圾邮件过滤实例_.vep [23.2M]
┃ ┣━━167.朴素贝叶斯算法整体框架_.vep [24.9M]
┃ ┣━━168.邮件数据读取_.vep [13.2M]
┃ ┣━━169.语料表与特征向量构建_.vep [25.5M]
┃ ┣━━170.分类别统计词频_.vep [23.4M]
┃ ┣━━171.贝叶斯公式对数变换_.vep [22.9M]
┃ ┣━━172.完成预测模块_.vep [26M]
┃ ┣━━173.关联规则概述_.vep [14.8M]
┃ ┣━━174.支持度与置信度_.vep [20.4M]
┃ ┣━━175.提升度的作用_.vep [63.2M]
┃ ┣━━176.Python实战关联规则_.vep [24.8M]
┃ ┣━━177.数据集制作_.vep [22.4M]
┃ ┣━━178.电影数据集题材关联分析_.vep [34.3M]
┃ ┣━━179.Apripri算法整体流程_.vep [26.4M]
┃ ┣━━180.数据集demo_.vep [9.2M]
┃ ┣━━181.扫描模块_.vep [33.3M]
┃ ┣━━182.拼接模块_.vep [16.2M]
┃ ┣━━183.挖掘频繁项集_.vep [44.4M]
┃ ┣━━184.规则生成模块_.vep [20.3M]
┃ ┣━━185.完成全部算法流程_.vep [22.1M]
┃ ┣━━186.规则结果展示_.vep [22.5M]
┃ ┣━━187.词向量模型通俗解释_.vep [16M]
┃ ┣━━188.模型整体框架_.vep [42.9M]
┃ ┣━━189.训练数据构建_.vep [12.3M]
┃ ┣━━190.CBOW与Skipgram模型_.vep [18.7M]
┃ ┣━━191.负采样方案_.vep [21M]
┃ ┣━━192.数据与任务流程_.vep [33.7M]
┃ ┣━━193.数据清洗_.vep [20.5M]
┃ ┣━━194.batch数据制作_.vep [37.2M]
┃ ┣━━195.网络训练_.vep [37.6M]
┃ ┣━━196.可视化展示_.vep [29.8M]
┃ ┣━━197.推荐系统应用_.vep [28.1M]
┃ ┣━━198.推荐系统要完成的任务_.vep [10.5M]
┃ ┣━━199.相似度计算_.vep [16.8M]
┃ ┣━━200.基于用户的协同过滤_.vep [14.7M]
┃ ┣━━201.基于物品的协同过滤_.vep [22.6M]
┃ ┣━━202.隐语义模型_.vep [11.9M]
┃ ┣━━203.隐语义模型求解_.vep [15.4M]
┃ ┣━━204.模型评估标准_.vep [11.4M]
┃ ┣━━205.音乐推荐任务概述_.vep [46.9M]
┃ ┣━━206.数据集整合_.vep [34.5M]
┃ ┣━━207.基于物品的协同过滤_.vep [44.5M]
┃ ┣━━208.物品相似度计算与推荐_.vep [119.7M]
┃ ┣━━209.SVD矩阵分解_.vep [42.7M]
┃ ┣━━210.基于矩阵分解的音乐推荐_.vep [56.1M]
┃ ┣━━211.线性判别分析要解决的问题_.vep [17M]
┃ ┣━━212.线性判别分析要优化的目标_.vep [19.6M]
┃ ┣━━213.线性判别分析求解_.vep [19.9M]
┃ ┣━━214.求解得出降维结果_.vep [23.3M]
┃ ┣━━215.PCA基本概念_.vep [33.2M]
┃ ┣━━216.方差与协方差_.vep [17.9M]
┃ ┣━━217.PCA结果推导_.vep [23.6M]
┃ ┗━━218.PCA降维实例_.vep [36.6M]
┗━━00.资料.zip [1G]

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