Named entity recognition (NER) is a classical task in NLP, which aims to identify meaningful chunks of text and classify them into appropriate entity types, such as PERSON or LOCATION. For instance, “Tom” is classified as “PERSON” while “England” is labelled as “LOCATION” in the sentence “Tom comes from England”. NER serves as the foundation for a range of downstream tasks such as information extraction. In practical applications, you may notice that some websites will autofill the application form from your resume.

We’ve talked about many ML algorithms and DL architectures so far, but how to find the optimal parameters? Mathematically, the process of minimizing the objective functions is called optimization, but it’s a bit different in DL — the global minimum point does not always achieve the best generalization performance. After all, we are minimizing the training error. Besides, the loss function typically is very complex, and there is no analytical solution to it. In this case, we have only one choice — optimization algorithms.

Recommender system is one of the most popular studying fields in machine learning due to its wide application in our daily life. When you shop online, such as Amazon, you can see similar items just below the item you are looking at. The goal of a recommender system is to make recommendations that fit the user’s taste. In this post, we will go through the very basic concepts and several classcial techniques in the field of recommender systems.

What and Why Nowadays, you will often see a statistic or dynamic image on your screen when you launch some APPs. The image looks like the figure below with a small button to enable people to skip ads. Generally, the showing time of ads lasts for at least 3 seconds. The most disgusting thing is that there is no upper bound for this, so you have to suffer from this unless you don’t use any app at all.

In linear regression, we choose $R^2$ as our metrics to measure how good our algorithms are. But how about classification? In this post, we are going to talk about several commonly used metrics for classification.