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Characteristic Engineering feature extraction Preprocessing of data features feature selection Why Feature Engineering The features in the sample data may have missing values, duplicate values, abnormal values, etc., so we need to process the noise data in the features. The purpose of processing is to obtain a purer sample set and mak ...

[source code analysis] machine learning parameter server PS Lite (2) -- communication module Van 0x00 summary This is the second part of the parameter server series, which introduces the communication module Van of PS Lite. Other articles in this series are: [source code analysis] machine learning parameter server PS Lite (1) -- postoff ...

Serial article: Last Vernacular machine learning concepts 1, Machine learning category Machine learning can be divided into supervised learning, unsupervised learning, semi supervised learning and reinforcement learning according to the difference of learning data experience, that is, the difference of label information of training data. 1.1 ...

summary It is said that this tutorial focuses on application. I haven't understood the principle yet. I read the first six chapters when eating melons. It's also vague..... sklearn is to switch packages. I hope I can learn. The process is: Instantiate and establish the evaluation model object;Train the model through the model interface;Extrac ...

Through the previous practice of in-depth learning, whether it is the CNN network built by ourselves or the official network model called through migration learning, it has its advantages and disadvantages. This experiment calls various common CNN network models to understand their characteristics and compare the accuracy of classification for ...

Introduction to Numpy Library NumPy is a powerful Python library, which is mainly used to perform calculations on multidimensional arrays. The word NumPy comes from two words -- Numerical and python. NumPy provides a large number of library functions and operations to help programmers easily perform Numerical calculations. It is widely used in ...

Lifting algorithm is a method to improve the accuracy of weak classification algorithm. This method first constructs a series of prediction functions, and then combines them into a prediction function in a certain way. Lifting algorithm is also a method to improve the accuracy of any given learning algorithm. It is an integrated algorithm. It ...

Integrated learning To be exact, ensemble learning is not a real machine learning algorithm, at least it does not introduce any new learning algorithm in essence. The overall idea of ensemble learning is to complete the final task through multiple basic (weak) learners. Just as it used to be a single challenge between an algorithm and a proble ...

2021SC@SDUSC In this analysis, I will briefly introduce and summarize the SimpleRecognizePlate function. Since this function is mainly analyzed by other members of my team, there is no need to analyze it in detail here. The focus is to compare it with the SimpleRecognizePlateByE2E function I analyzed. The function code is as follows: def Sim ...

Shape Detection Contour detection contours, hierarchy = cv2.findContours(image,mode,method) The first parameter input image,The second parameter represents the retrieval mode of contour, which has four types: 1.cv2.RETR_EXTERNAL means that only the outer contour is detected 2.cv2. RETR_ The contour detected by list does not establish a hiera ...