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# 机器学习
## 机器学习基础
## Jupyter Notebook, numpy和matplotlib
## 最基础的分类算法-k近邻算法 kNN
## 线性回归法
## 梯度下降法
## PCA与梯度上升法
## 多项式回归与模型泛化
## 逻辑回归
## 评价分类结果
## 支撑向量机 SVM
## 决策树
## 集成学习和随机森林
## 入门必修:单、多层感知机
## 深度学习基础组件精讲
## 图像处理利器:卷积神经网络
## 为序列数据而生RNN系列
## 深度学习新思路: GAN网络
## 赋予模型认知能力:注意力机制
## 数据不够怎么办?迁移学习来帮忙
## 深度学习新范式:半监督学习
##