机器学习思维导图

此思维导图可以作为机器学习的参考，让学习者可以有一个整体的观念

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机器学习

监督学习

无监督学习

半监督学习

通用框架

类型

收集数据集

建立模型

确定损失函数

使用优化方法

预测和检验

线性模型

非线性模型

多项式模型

样条模型

回归树模型

神经网络模型

深层神经网络

循环神经网络

卷积神经网络

反向传播神经网络

平方损失函数

梯度下降法

梯度提升法

最大期望法

基于统计学的方法

基于机器学习的方法

分类_离散值

回归_连续值

聚类分析

主成分分析

注：目前的内容还是个半成品，大家如果有新颖思路的话，可以在评论区留言，我会进一步改进

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机器学习思维导图（更新中）

机器学习思维导图

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