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： 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： 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.