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数据科学的一点学习笔记 （持续更新中）

备注

  • 文件格式：本来我的笔记是写在有道云笔记里的，它的markdown有微调。 我自己试了试pycharm的markdown，看不了目录跟公式， 大家按自己的编辑器康康怎么弄吧
  • 阅读顺序：如果想系统的学习/复习，建议按照我的目录顺序来
  • 内容方面：机器学习跟nlp方面已经有比较完整的笔记，会陆续搬到github。 cv方面不是很了解，后续可能会补充。
  • 求助：计算机基础方面跪求各位大佬分享资源orz

目录

math @toc

  • 数学基础

machine_learning @toc

  • 机器学习_理论 @toc
  • 机器学习_KNN @toc
  • 机器学习_回归模型 @toc
  • 机器学习_分类模型 @toc
  • 机器学习_聚类算法 @toc
  • 机器学习_集成学习 @toc
  • 机器学习_梯度下降 @toc
  • 机器学习_降维 @toc 待补充完整
  • 机器学习_常见损失函数 @toc
  • 机器学习_常见机器学习模型的特点 @toc
  • 机器学习_感知机 待补充

deep_learning @toc

  • 深度学习_神经网络的收敛@toc
  • 深度学习_常见问题 @toc
  • 深度学习_项目实践杂谈 @toc
  • 深度学习_MLP @toc
  • 深度学习_CNN @toc
  • 深度学习_RNN @toc
  • 深度学习_LSTM @toc
  • 深度学习_GRU @toc
  • 深度学习_迁移学习 #TODO
  • 自然语言处理_知识蒸馏 #TODO
  • 自然语言处理_知识图谱 #TODO

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