机器学习基础
1 [特征值分解、奇异值分解、PCA概念整理]
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]
4 [机器学习中牛顿法凸优化的通俗解释]
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]
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]
10 [【面向代码】学习 Deep Learning（一）Neural Network]
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]
16 [入门 | 深度学习模型的简单优化技巧]
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]
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]
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]
28 [从PCA和SVD的关系拾遗]
29 [深入解析数据压缩算法]
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]
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
