机器学习相关博客收集

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机器学习基础

1 [特征值分解、奇异值分解、PCA概念整理](https://blog.csdn.net/jinshengtao/article/details/18448355)https://blog.csdn.net/jinshengtao/article/details/18448355

2 [数学之美番外篇：平凡而又神奇的贝叶斯方法](http://mindhacks.cn/2008/09/21/the-magical-bayesian-method/)http://mindhacks.cn/2008/09/21/the-magical-bayesian-method/

3 [从最大似然到EM算法浅解](https://blog.csdn.net/zouxy09/article/details/8537620)https://blog.csdn.net/zouxy09/article/details/8537620

4 [机器学习中牛顿法凸优化的通俗解释](https://blog.csdn.net/red_stone1/article/details/80821760)https://blog.csdn.net/red_stone1/article/details/80821760

5 [大白话解释模型产生过拟合的原因](https://mp.weixin.qq.com/s?__biz=MzI4MDYzNzg4Mw==&mid=2247487846&idx=1&sn=5d031de2071490edff95f0c270ff18d0&chksm=ebb429b2dcc3a0a49fe3c44b376e32fd7e437fe4aa395d01a10a940018af4ff3b399d93fb071&scene=38#wechat_redirect)https://mp.weixin.qq.com/s?__biz=MzI4MDYzNzg4Mw==&mid=2247487846&idx=1&sn=5d031de2071490edff95f0c270ff18d0&chksm=ebb429b2dcc3a0a49fe3c44b376e32fd7e437fe4aa395d01a10a940018af4ff3b399d93fb071&scene=38#wechat_redirect

6 [使用 RNN 进行情感分析的初学者指南](https://mp.weixin.qq.com/s?__biz=MjM5ODU3OTIyOA==&mid=2650671817&idx=1&sn=12d082dca4aed7fc0fb25bdf307e93ee&chksm=bec237ba89b5beac972d6a6a225e7884c142d658f64e7862dda563d9d78fe6587995e2c06eec&scene=38#wechat_redirect)https://mp.weixin.qq.com/s?__biz=MjM5ODU3OTIyOA==&mid=2650671817&idx=1&sn=12d082dca4aed7fc0fb25bdf307e93ee&chksm=bec237ba89b5beac972d6a6a225e7884c142d658f64e7862dda563d9d78fe6587995e2c06eec&scene=38#wechat_redirect

7 [理解机器学习算法的一点心得](https://blog.csdn.net/dark_scope/article/details/25485893)https://blog.csdn.net/dark_scope/article/details/25485893

8 [利用 AutoAugment 提升深度学习性能](https://mp.weixin.qq.com/s?__biz=MzU1OTMyNDcxMQ==&mid=2247484414&idx=1&sn=8aaf3523c9fac10908ee5258bf58b4d1&chksm=fc1848b6cb6fc1a0e3eb6d22c6dcf5f5f23811056ee7d20614c74834c57e1737b6cb508bb59e&scene=38#wechat_redirect)https://mp.weixin.qq.com/s?__biz=MzU1OTMyNDcxMQ==&mid=2247484414&idx=1&sn=8aaf3523c9fac10908ee5258bf58b4d1&chksm=fc1848b6cb6fc1a0e3eb6d22c6dcf5f5f23811056ee7d20614c74834c57e1737b6cb508bb59e&scene=38#wechat_redirect

9 [聚类之详解FCM算法原理及应用](https://blog.csdn.net/on2way/article/details/47087201)https://blog.csdn.net/on2way/article/details/47087201

10 [【面向代码】学习 Deep Learning（一）Neural Network](https://blog.csdn.net/dark_scope/article/details/9421061)https://blog.csdn.net/dark_scope/article/details/9421061

11 [含代码 | 支付宝如何优化移动端深度学习引擎？](https://mp.weixin.qq.com/s/jqRBrs9Y_-3qvemL0RTflA)https://mp.weixin.qq.com/s/jqRBrs9Y_-3qvemL0RTflA

12 [极简梯度下降教程](https://mp.weixin.qq.com/s/YuNUwK9tEEds847TmR4zOw)https://mp.weixin.qq.com/s/YuNUwK9tEEds847TmR4zOw

13 [如何选择合适的损失函数，请看......](https://mp.weixin.qq.com/s/Gt8Q4Wm36DoNBO4xI8SJAw)https://mp.weixin.qq.com/s/Gt8Q4Wm36DoNBO4xI8SJAw

14 [如何使用 Keras 实现无监督聚类](https://mp.weixin.qq.com/s/KEHTZCWM1ygUpUoVE0RJxA)https://mp.weixin.qq.com/s/KEHTZCWM1ygUpUoVE0RJxA

15 [交叉熵代价函数(损失函数)及其求导推导](https://blog.csdn.net/jasonzzj/article/details/52017438)https://blog.csdn.net/jasonzzj/article/details/52017438

16 [入门 | 深度学习模型的简单优化技巧](https://blog.csdn.net/Z4a9Gx/article/details/80650130)https://blog.csdn.net/Z4a9Gx/article/details/80650130

17 [一小时神经网络从入门到精通(放弃)](https://yq.aliyun.com/articles/602865?spm=a2c4e.11157919.spm-cont-list.17.146c27aexBjDTq)https://yq.aliyun.com/articles/602865?spm=a2c4e.11157919.spm-cont-list.17.146c27aexBjDTq

18 [随机加权平均 -- 在深度学习中获得最优结果的新方法](https://mp.weixin.qq.com/s/tjUHr8l-oHn_5lw-tQW4cA)https://mp.weixin.qq.com/s/tjUHr8l-oHn_5lw-tQW4cA

19 [解密SVM系列（四）：SVM非线性分类原理实验](https://blog.csdn.net/on2way/article/details/47731455)https://blog.csdn.net/on2way/article/details/47731455

20 [不同的领域、框架，这是一份超全的深度学习模型GitHub集合](https://mp.weixin.qq.com/s/iszzOubuS0PJ35jQzddmSg)https://mp.weixin.qq.com/s/iszzOubuS0PJ35jQzddmSg

21 [详解深度学习中的常用优化算法](https://mp.weixin.qq.com/s/Bu9GDxQQRaw74uLFPteI5w)https://mp.weixin.qq.com/s/Bu9GDxQQRaw74uLFPteI5w

22 [马尔科夫决策过程之Markov Reward Process（马尔科夫奖励过程）](https://zhuanlan.zhihu.com/p/35231424)https://zhuanlan.zhihu.com/p/35231424

23 [通俗理解信息熵](https://mp.weixin.qq.com/s/6I2-5zWbpAUSgoHRUEkj3Q)https://mp.weixin.qq.com/s/6I2-5zWbpAUSgoHRUEkj3Q

24 [从贝叶斯理论到图像马尔科夫随机场](https://blog.csdn.net/on2way/article/details/47307927)https://blog.csdn.net/on2way/article/details/47307927

25 [资源 | 跟着Sutton经典教材学强化学习中的蒙特卡罗方法（代码实例）](https://mp.weixin.qq.com/s?__biz=MjM5MTQzNzU2NA==&mid=2651660556&idx=2&sn=3befd9f38dd8f8c665bcd792f4be04c8&chksm=bd4c069f8a3b8f8909e6528cae05252074cb1aee6a092dd393da1ca4d006163f1af657c7c30b&scene=0#rd)https://mp.weixin.qq.com/s?__biz=MjM5MTQzNzU2NA==&mid=2651660556&idx=2&sn=3befd9f38dd8f8c665bcd792f4be04c8&chksm=bd4c069f8a3b8f8909e6528cae05252074cb1aee6a092dd393da1ca4d006163f1af657c7c30b&scene=0#rd

26 [浅谈贝叶斯和MCMC](https://mp.weixin.qq.com/s?__biz=MzI0ODcxODk5OA==&mid=2247495395&idx=3&sn=f9b69332eab60127f84db60862f28bc4&chksm=e99edd1adee9540c9dd3f9734c6e5e628800a10cec9191fd0dfb434a1fd5b8cf0cfedffd6a69&scene=0#rd)https://mp.weixin.qq.com/s?__biz=MzI0ODcxODk5OA==&mid=2247495395&idx=3&sn=f9b69332eab60127f84db60862f28bc4&chksm=e99edd1adee9540c9dd3f9734c6e5e628800a10cec9191fd0dfb434a1fd5b8cf0cfedffd6a69&scene=0#rd

27 [机器学习之实战朴素贝叶斯算法](https://blog.csdn.net/on2way/article/details/47294805)https://blog.csdn.net/on2way/article/details/47294805

28 [从PCA和SVD的关系拾遗](https://blog.csdn.net/dark_scope/article/details/53150883)https://blog.csdn.net/dark_scope/article/details/53150883

29 [深入解析数据压缩算法](https://blog.csdn.net/fanyun_01/article/details/80211799)https://blog.csdn.net/fanyun_01/article/details/80211799

30 [讲讲频率和概率以及均值和期望的联系区别](https://mp.weixin.qq.com/s/hz4HSkiV05790_KT_x-R0w)https://mp.weixin.qq.com/s/hz4HSkiV05790_KT_x-R0w

31 [深度学习入门必须理解这25个概念](https://blog.csdn.net/pangjiuzala/article/details/72630166)https://blog.csdn.net/pangjiuzala/article/details/72630166

32 [走近流行强化学习算法：最优Q-Learning](https://www.itcodemonkey.com/article/4095.html)https://www.itcodemonkey.com/article/4095.html

33 [这样学深度学习中激活函数，一切就顺起来了！知识点并不是突兀而来](https://mp.weixin.qq.com/s?__biz=MzI4MDYzNzg4Mw==&mid=2247487929&idx=1&sn=b2ebdb2b84fe3f26391004e9b9cf5573&chksm=ebb4296ddcc3a07b986ae758fb0cd5c484d10efe602247bdd5d384e4ff32db910633132495ef&scene=38#wechat_redirect)https://mp.weixin.qq.com/s?__biz=MzI4MDYzNzg4Mw==&mid=2247487929&idx=1&sn=b2ebdb2b84fe3f26391004e9b9cf5573&chksm=ebb4296ddcc3a07b986ae758fb0cd5c484d10efe602247bdd5d384e4ff32db910633132495ef&scene=38#wechat_redirect