风雨中的小七

摘要： 想要从本质上解决小样本估计不置信的问题，一个更符合思维逻辑的方法是我们先基于经验给出一个预期估计，然后不断用收集到的样本来对我们的预期进行更新，这样在样本量小的时候，样本不会对我们的预期有较大影响，估计值会近似于我们预先设定的经验值，从而避免像小样本估计不置信的问题。 阅读全文

摘要： 在第一篇打分系统漫谈1 - 时间衰减我们聊了两种相对简单的打分算法Hacker News和Reddit Hot Formula，也提出了几个这两种算法可能存在的问题，这一篇我们就其中的两一个问题进一步讨论： 如何综合浏览量和点赞量对文章进行打分[期望效用函数->点赞率] 如何解决浏览量较小时，点赞率不置信的问题[wald Interval -> wilson] 阅读全文

摘要： 打分排序系统的应用非常普遍，比如电影的评分，知乎帖子的热度，和新闻文章的排序。让我们从最简单直观的平均打分开始， 聊聊各种打分方法的利弊和使用场景。 阅读全文

摘要： In the previous post, we talk about a very popular Boosting algorithm [Gradient Boosting Decision Tree][1]. The key of GBM is using Gradient Descent t 阅读全文

摘要： This is the second post in Boosting algorithm. In the previous post, we go through the earliest Boosting algorithm [AdaBoost][1], which is actually an 阅读全文

摘要： In the previous post we addressed some issue of decision tree, including instability, lack of smoothness, sensitivity to data, and etc. One solution i 阅读全文

摘要： After talking about Information theory, now let's come to one of its application Decision Tree! Nowadays, in terms of prediction power, there are many 阅读全文

摘要： This will be a series of post about Tree model and relevant ensemble method, including but not limited to Random Forest, AdaBoost, Gradient Boosting a 阅读全文

摘要： I have had a hard time trying to understand recurrent model. Compared to Ng's deep learning course, I found Deep Learning book written by Ian, Yoshua 阅读全文

摘要： Finally pass all the Deeplearning.ai courses in March! I highly recommend it! If you already know the basic then you may be interested in course 4 & 5 阅读全文