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01-频率视角下的机器学习[防断更微lxknumber1].md
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01-频率视角下的机器学习[防断更微lxknumber1].mp3
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01-频率视角下的机器学习[防断更微lxknumber1].pdf
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02-贝叶斯视角下的机器学习[防断更微lxknumber1].md
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02-贝叶斯视角下的机器学习[防断更微lxknumber1].mp3
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02-贝叶斯视角下的机器学习[防断更微lxknumber1].pdf
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03-学什么与怎么学[防断更微lxknumber1].md
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03-学什么与怎么学[防断更微lxknumber1].mp3
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03-学什么与怎么学[防断更微lxknumber1].pdf
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04-计算学习理论[防断更微lxknumber1].md
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04-计算学习理论[防断更微lxknumber1].mp3
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04-计算学习理论[防断更微lxknumber1].pdf
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05-模型的分类方式[防断更微lxknumber1].md
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05-模型的分类方式[防断更微lxknumber1].mp3
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05-模型的分类方式[防断更微lxknumber1].pdf
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06-模型的设计准则[防断更微lxknumber1].md
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06-模型的设计准则[防断更微lxknumber1].mp3
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06-模型的设计准则[防断更微lxknumber1].pdf
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07-模型的验证方法[防断更微lxknumber1].md
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07-模型的验证方法[防断更微lxknumber1].mp3
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07-模型的验证方法[防断更微lxknumber1].pdf
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08-模型的评估指标[防断更微lxknumber1].md
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08-模型的评估指标[防断更微lxknumber1].mp3
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08-模型的评估指标[防断更微lxknumber1].pdf
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09-实验设计[防断更微lxknumber1].md
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09-实验设计[防断更微lxknumber1].mp3
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09-实验设计[防断更微lxknumber1].pdf
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10-特征预处理[防断更微lxknumber1].md
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10-特征预处理[防断更微lxknumber1].mp3
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10-特征预处理[防断更微lxknumber1].pdf
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11-基础线性回归：一元与多元[防断更微lxknumber1].md
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11-基础线性回归：一元与多元[防断更微lxknumber1].mp3
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11-基础线性回归：一元与多元[防断更微lxknumber1].pdf
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12-正则化处理：收缩方法与边际化[防断更微lxknumber1].md
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12-正则化处理：收缩方法与边际化[防断更微lxknumber1].mp3
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12-正则化处理：收缩方法与边际化[防断更微lxknumber1].pdf
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13-线性降维：主成分的使用[防断更微lxknumber1].md
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13-线性降维：主成分的使用[防断更微lxknumber1].mp3
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13-线性降维：主成分的使用[防断更微lxknumber1].pdf
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14-非线性降维：流形学习[防断更微lxknumber1].md
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14-非线性降维：流形学习[防断更微lxknumber1].mp3
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14-非线性降维：流形学习[防断更微lxknumber1].pdf
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15-从回归到分类：联系函数与降维[防断更微lxknumber1].md
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15-从回归到分类：联系函数与降维[防断更微lxknumber1].mp3
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15-从回归到分类：联系函数与降维[防断更微lxknumber1].pdf
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16-建模非正态分布：广义线性模型[防断更微lxknumber1].md
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16-建模非正态分布：广义线性模型[防断更微lxknumber1].mp3
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16-建模非正态分布：广义线性模型[防断更微lxknumber1].pdf
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17-几何角度看分类：支持向量机[防断更微lxknumber1].md
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17-几何角度看分类：支持向量机[防断更微lxknumber1].mp3
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17-几何角度看分类：支持向量机[防断更微lxknumber1].pdf
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18-从全局到局部：核技巧[防断更微lxknumber1].md
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18-从全局到局部：核技巧[防断更微lxknumber1].mp3
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18-从全局到局部：核技巧[防断更微lxknumber1].pdf
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19-非参数化的局部模型：K近邻[防断更微lxknumber1].md
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19-非参数化的局部模型：K近邻[防断更微lxknumber1].mp3
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19-非参数化的局部模型：K近邻[防断更微lxknumber1].pdf
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20-基于距离的学习：聚类与度量学习[防断更微lxknumber1].md
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20-基于距离的学习：聚类与度量学习[防断更微lxknumber1].mp3
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20-基于距离的学习：聚类与度量学习[防断更微lxknumber1].pdf
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21-基函数扩展：属性的非线性化[防断更微lxknumber1].md
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21-基函数扩展：属性的非线性化[防断更微lxknumber1].mp3
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21-基函数扩展：属性的非线性化[防断更微lxknumber1].pdf
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22-自适应的基函数：神经网络[防断更微lxknumber1].md
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22-自适应的基函数：神经网络[防断更微lxknumber1].mp3
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22-自适应的基函数：神经网络[防断更微lxknumber1].pdf
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23-层次化的神经网络：深度学习[防断更微lxknumber1].md
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23-层次化的神经网络：深度学习[防断更微lxknumber1].mp3
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23-层次化的神经网络：深度学习[防断更微lxknumber1].pdf
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24-深度编解码：表示学习[防断更微lxknumber1].md
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24-深度编解码：表示学习[防断更微lxknumber1].mp3
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24-深度编解码：表示学习[防断更微lxknumber1].pdf
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25-基于特征的区域划分：树模型[防断更微lxknumber1].md
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25-基于特征的区域划分：树模型[防断更微lxknumber1].mp3
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25-基于特征的区域划分：树模型[防断更微lxknumber1].pdf
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26-集成化处理：Boosting与Bagging[防断更微lxknumber1].md
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26-集成化处理：Boosting与Bagging[防断更微lxknumber1].mp3
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26-集成化处理：Boosting与Bagging[防断更微lxknumber1].pdf
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27-万能模型：梯度提升与随机森林[防断更微lxknumber1].md
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27-万能模型：梯度提升与随机森林[防断更微lxknumber1].mp3
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27-万能模型：梯度提升与随机森林[防断更微lxknumber1].pdf
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28-最简单的概率图：朴素贝叶斯[防断更微lxknumber1].md
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28-最简单的概率图：朴素贝叶斯[防断更微lxknumber1].mp3
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28-最简单的概率图：朴素贝叶斯[防断更微lxknumber1].pdf
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29-有向图模型：贝叶斯网络[防断更微lxknumber1].md
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29-有向图模型：贝叶斯网络[防断更微lxknumber1].mp3
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29-有向图模型：贝叶斯网络[防断更微lxknumber1].pdf
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30-无向图模型：马尔可夫随机场[防断更微lxknumber1].md
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30-无向图模型：马尔可夫随机场[防断更微lxknumber1].mp3
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30-无向图模型：马尔可夫随机场[防断更微lxknumber1].pdf
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31-建模连续分布：高斯网络[防断更微lxknumber1].md
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31-建模连续分布：高斯网络[防断更微lxknumber1].mp3
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31-建模连续分布：高斯网络[防断更微lxknumber1].pdf
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32-从有限到无限：高斯过程[防断更微lxknumber1].md
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32-从有限到无限：高斯过程[防断更微lxknumber1].mp3
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32-从有限到无限：高斯过程[防断更微lxknumber1].pdf
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33-序列化建模：隐马尔可夫模型[防断更微lxknumber1].md
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33-序列化建模：隐马尔可夫模型[防断更微lxknumber1].mp3
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33-序列化建模：隐马尔可夫模型[防断更微lxknumber1].pdf
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34-连续序列化模型：线性动态系统[防断更微lxknumber1].md
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34-连续序列化模型：线性动态系统[防断更微lxknumber1].mp3
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34-连续序列化模型：线性动态系统[防断更微lxknumber1].pdf
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35-精确推断：变量消除及其拓展[防断更微lxknumber1].md
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35-精确推断：变量消除及其拓展[防断更微lxknumber1].mp3
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35-精确推断：变量消除及其拓展[防断更微lxknumber1].pdf
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36-确定近似推断：变分贝叶斯[防断更微lxknumber1].md
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36-确定近似推断：变分贝叶斯[防断更微lxknumber1].mp3
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36-确定近似推断：变分贝叶斯[防断更微lxknumber1].pdf
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37-随机近似推断：MCMC[防断更微lxknumber1].md
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37-随机近似推断：MCMC[防断更微lxknumber1].mp3
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37-随机近似推断：MCMC[防断更微lxknumber1].pdf
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38-完备数据下的参数学习：有向图与无向图[防断更微lxknumber1].md
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38-完备数据下的参数学习：有向图与无向图[防断更微lxknumber1].mp3
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38-完备数据下的参数学习：有向图与无向图[防断更微lxknumber1].pdf
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39-隐变量下的参数学习：EM方法与混合模型[防断更微lxknumber1].md
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39-隐变量下的参数学习：EM方法与混合模型[防断更微lxknumber1].mp3
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39-隐变量下的参数学习：EM方法与混合模型[防断更微lxknumber1].pdf
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40-结构学习：基于约束与基于评分[防断更微lxknumber1].md
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40-结构学习：基于约束与基于评分[防断更微lxknumber1].mp3
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40-结构学习：基于约束与基于评分[防断更微lxknumber1].pdf
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结课-终有一天，你将为今天的付出骄傲[防断更微lxknumber1].md
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结课-终有一天，你将为今天的付出骄傲[防断更微lxknumber1].mp3
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结课-终有一天，你将为今天的付出骄傲[防断更微lxknumber1].pdf
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结课测试-这些机器学习知识你都掌握了吗？[防断更微lxknumber1].md
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结课测试-这些机器学习知识你都掌握了吗？[防断更微lxknumber1].pdf
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开篇词-打通修炼机器学习的任督二脉[防断更微lxknumber1].md
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开篇词-打通修炼机器学习的任督二脉[防断更微lxknumber1].mp3
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开篇词-打通修炼机器学习的任督二脉[防断更微lxknumber1].pdf
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每天两小时，副业收入过万咨询+V.png
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如何成为机器学习工程师？[防断更微lxknumber1].md
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如何成为机器学习工程师？[防断更微lxknumber1].mp3
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如何成为机器学习工程师？[防断更微lxknumber1].pdf
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微信lxknumber1，避免失联、断更.png
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总结课-贝叶斯学习的模型体系[防断更微lxknumber1].md
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总结课-贝叶斯学习的模型体系[防断更微lxknumber1].mp3
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总结课-贝叶斯学习的模型体系[防断更微lxknumber1].pdf
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总结课-机器学习的模型体系[防断更微lxknumber1].md
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总结课-机器学习的模型体系[防断更微lxknumber1].mp3
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总结课-机器学习的模型体系[防断更微lxknumber1].pdf
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