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Course Introduction:"Self-study IT Network Linux Load Balancing Video Tutorial" mainly implements Linux load balancing by performing script operations on web, lvs and Linux under nagin.
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Course Introduction:"Shangxuetang MySQL Video Tutorial" introduces you to the process from installing to using the MySQL database, and introduces the specific operations of each link in detail.
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Course Introduction:"Brothers Band Front-end Example Display Video Tutorial" introduces examples of HTML5 and CSS3 technologies to everyone, so that everyone can become more proficient in using HTML5 and CSS3.
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javascript - An algorithm question in front-end interview
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javascript - python small algorithm
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Course Introduction:这篇文章主要介绍了Python聚类算法之凝聚层次聚类的原理与具体使用技巧,具有一定参考借鉴价值,需要的朋友可以参考下
2016-06-10 comment 0 3395
Course Introduction:How to write K-means clustering algorithm in Python? K-means clustering algorithm is a commonly used data mining and machine learning algorithm that can classify and cluster a set of data according to its attributes. This article will introduce how to write the K-means clustering algorithm in Python and provide specific code examples. Before we start writing code, we need to understand the basic principles of K-means clustering algorithm. The basic steps of the K-means clustering algorithm are as follows: Initialize k centroids. The centroid refers to the center point of the cluster, and each data point will be assigned to its closest
2023-09-21 comment 0 863
Course Introduction:How to implement the DBSCAN clustering algorithm using Python? DBSCAN (Density-BasedSpatialClusteringofApplicationswithNoise) is a density-based clustering algorithm that can automatically identify data points with similar densities and divide them into different clusters. Compared with traditional clustering algorithms, DBSCAN shows higher flexibility and robustness in processing non-spherical and irregularly shaped data sets. Book
2023-09-19 comment 0 943
Course Introduction:The clustering effect evaluation problem in the clustering algorithm requires specific code examples. Clustering is an unsupervised learning method that groups similar samples into one category by clustering data. In clustering algorithms, how to evaluate the effect of clustering is an important issue. This article will introduce several commonly used clustering effect evaluation indicators and give corresponding code examples. 1. Clustering effect evaluation index Silhouette Coefficient Silhouette coefficient evaluates the clustering effect by calculating the closeness of the sample and the degree of separation from other clusters.
2023-10-10 comment 0 885
Course Introduction:这篇文章主要介绍了Python聚类算法之DBSACN,结合实例形式详细分析了DBSACN算法的原理与具体实现技巧,具有一定参考借鉴价值,需要的朋友可以参考下
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