Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience.
In this page, we write some tutorials and examples on machine learning algorithms and applications. You can learn how to use machine learning in life by following our tutorials.
Attention mechanism is an important method to improve the performance of deep learning model. There are two forms of attention, which one we should use? In this tutorial, we will find some tips.
In deep learning, we often use a vector to express a target feature, however, how to fuse them if we have got some features? In this tutorial, we will discuss this topic.
Out-Of-Vocabulary (OOV) words is an important problem in NLP, we will introduce how to process words that are out of vocabulary in this tutorial.
Position is an important feature for deep learning model. For example, in aspect level sentiment analyis, we can use word position to improve the efficiency of classification. In this tutorial, we will introduce how to use position in deep learning model.
Sentiment lexicons are important resources to improve the efficiency of sentiment analysis. In this tutorial, we will list some useful sentiment lexicons.
Can we compute a probability by distance? In this tutorial, we will discuss the relationship of duclidean distance and gaussian distribution, which will help us to convert a distance to a probability.
Multivarible chain rule is a good way to analyze the derivative of a machine learning model. In this tutorial, we will introduce it for machine learning beginners.
In this tutorial, we will introduce diagonal matrice, upper and lower triangular matrice in Linear Algebra for beginners.
In this tutorial, we will introdue the implementation of basic RNN in tensorflow. You can understand it more clearly.
Batch normalization is proposed in paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In this tutorial, we will explain it for machine learning beginners.