摘要： 去年NLP領域最火的莫過於BERT了，得益於數據規模和計算力的提升，BERT在大規模語料上預訓練（Masked Language Model + Next Sentence Prediction）之後可以很好地從訓練語料中捕獲豐富的語義信息，對各項任務瘋狂屠榜。我們在對BERT進行微調之後可以很好地適用到自己的任務上。如果想深入了解BERT的運行機制，就需要去仔細地研讀一下BERT的源碼。今天這篇文章我們來看看在BERT提出大半年之後，又有哪些基於BERT的有趣的研究。

摘要： 轉載自程序員書庫機器學習算是一個交叉領域，他涉及統計、概率、計算機科學和算法等方面，近幾年機器學習發展快速，有人就想要入門學習數據科學領域，使用機器學習（ML）技術創造產品，但是想要很好的掌握其內部的工作原理和算法，有個堅固的數學基礎是很有必要的。

摘要： Reinforcement learning is a type of machine learning in whicha computer learns to perform a task through repeated trial-and-error interactions with a dynamic environment. This learning approach enables the computer to make a series of decisions that maximize a reward metric for the task without human intervention and without being explicitly programmed to achieve the task.

摘要： 堆疊是集成多個分類法或回歸模型的方式。有很多方法可以集成模型，眾所周知的模型有Bagging或Boosting。Bagging允許多個具有高方差類似的分類模型中取平均以減少差異。Boosting建立多個增量模型，以減少誤差，同時保持方差小。

摘要： You probably used random forest for regression and classification before, but time series forecasting? Hold up you’re going to say; time series data is special! And you’re right. When it comes to data that has a time dimension, applying machine learning (ML) methods becomes a little tricky.....

摘要： Imbalanced classes are a common problem in machine learning classification where there are a disproportionate ratio of observations in each class. Class imbalance can be found in many different areas including medical diagnosis, spam filtering, and wildfire detection.

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