The simplest way to understand Variational Inference

Overview: Generally speaking, Variational Inference(VI) is a specific Statistical inference method that attempts to find the best tractable distribution to replace the intractable distribution when the unknown density function is intractable. In this way, a typical inference problem can be converted to a typical optimization problem. The best tractable distribution we look for is the

My new paper: Anomaly Detection based on LDA, Autoencoder and GMM

Recently my team has finished a paper about Anomaly Detection. We proposed a novel unsupervised Anomaly Detection model (LAG) based on LDA, Autoencoder, and GMM. Our model can be used on both structured and unstructured data and provides a comprehensive solution for various Anomaly Detection tasks in different industries. Particularly, we provide a way to

Shape Data for RNN by a single function

When we attempt to construct an RNN model in the Tensorflow. We will always come across a task that is reshaping the data to meet the input format of the RNN model. This step is essential but a little confusing. To simplify the work of reshaping data for RNN, I develop an RNN sample generator

The simplest way to understand SVM

Recently, many friends asked me how to understand SVM intuitively. They were confused by the complicated mathematical formulas. Today I will thoroughly explain what is SVM through eight questions. For those who need to know the mathematical proofs, please refer to the appendix below. Q1: How to understand SVM most intuitively？ Ans1: Firstly, take a

Suspicious customer detection with LDA + Auto-Encoder part1

Suspicious customer detection is one of the applications of Fraud Detection or Anomaly Detection. There are many data-driven approaches in this area. This blog will introduce a very novel approach as a good alternative solution for suspicious customer detection: LDA + Auto-Encoder. Different business units or business lines have different perspectives to detect anomaly customers.