Course2110
Course Introduction:If you have any questions, add WeChat: Le-studyg; this course is a course for Swoole extension, aiming to deeply explore the Swoole multi-process model and its implementation principles. Through this course, learners will understand the concepts, principles and applications of the multi-process model in the Swoole framework. The course content covers the basic concepts of the Swoole multi-process model, inter-process communication, process management, process pools, etc., helping learners comprehensively master the technical points of Swoole multi-process programming, so as to better apply it to actual projects. Through the study of this course, students will be able to have a deeper understanding of the Swoole multi-process model and provide strong support for the development of high-performance, high-concurrency network applications.
Course19930
Course Introduction:"BootStrap Classic Case Analysis" This course was recorded by Beifeng.com. Bootstrap is based on HTML, CSS, and JavaScript. It is simple and flexible, making web development faster. Bootstrap provides elegant HTML and CSS specifications, which are written in the dynamic CSS language Less. Bootstrap has become very popular since its launch and has always been a popular open source project on GitHub.
Course2330
Course Introduction:If you have any questions, please contact WeChat: Le-studyg; in-depth exploration of PHP source code and understanding of its internal working principles is the path to advancement for every PHP developer. This course will lead students to analyze the source code of PHP (php-src), and comprehensively analyze the underlying implementation of PHP from core language features to extension mechanisms. By analyzing the source code, students will gain an in-depth understanding of PHP's internal mechanisms, such as memory management, garbage collection, function calls, etc., thereby improving their understanding of PHP performance tuning and extension development. This course is suitable for developers who have a certain foundation in PHP and who want to deeply understand the internal principles of PHP and improve their technical level. Let us explore the source code world of PHP and uncover the mystery of PHP!
Course4007
Course Introduction:Axios is currently the most popular request tool on the front end, used to send AJAX requests to the server for data exchange. In this course, you can learn axios API and axios source code analysis. If you have mastered the basic usage, you can directly learn the source code analysis part and simulate important functions.
Course7339
Course Introduction:This set of courses is designed to help you analyze the laravel framework source code. The course content includes optimized containers, decoration mode, request-credit agent, framework load free env environment, exception mechanism, etc.
2023-10-02 13:14:52 0 0 206
"Converting JavaScript Variables to PHP: JavasScript variables converted to PHP"
2023-09-18 11:50:08 0 1 183
**TemplateSyntaxError at /shop/** Unable to parse remaining part: '' from 'product
2023-09-15 16:44:57 0 1 261
"Laravel One-To-Many relationships require data to be in the Model::all() output"
2023-09-14 22:56:53 0 1 346
Compare values in duplicate tables
2023-09-13 08:52:32 0 2 259
Course Introduction:Concept Binary search tree is also called binary sorting tree. It is either an empty tree or a binary tree with the following properties: 1. If its left subtree is not empty, then the values of all nodes on the left subtree are Less than the value of the root node. 2. If its right subtree is not empty, the values of all nodes on the right subtree are greater than the value of the root node. 3. Its left and right subtrees are also directly prepared for the practice of binary search trees: defining the class of a tree node and the class of the binary search tree. The search function of searching a binary tree assumes that we have constructed such a binary tree, as shown in the figure below. The first question we have to think about is how to find whether a certain value is in the binary tree? According to the above logic, let's carry out the search method Complete. According to the above logic, let’s write the insertion operation of searching the binary tree
2023-05-07 comment 0571
Course Introduction:Dependency tree feature extraction is a commonly used technique in natural language processing to extract useful features from text. Dependency tree is a tool that represents the grammatical dependencies between words in a sentence. This article will introduce the concepts, applications and techniques of dependency tree feature extraction. A dependency tree is a directed acyclic graph that represents the dependencies between words. In a dependency tree, each word is a node and each dependency is a directed edge. Dependencies can be the result of tasks such as part-of-speech tagging, named entity recognition, syntactic analysis, etc. Dependency trees can be used to represent the grammatical structure between words in a sentence, including subject-predicate relationships, verb-object relationships, attributive clauses, etc. Syntactic features in sentences can be extracted by analyzing dependency trees, and these features can be used for various tasks in natural language processing, such as text segmentation.
2024-01-23 comment 0870
Course Introduction:Translator | Reviewed by Zhao Qingyu | Sun Shujuan Preface In machine learning, classification has two stages, namely the learning stage and the prediction stage. In the learning phase, a model is built based on the given training data; in the prediction phase, the model is used to predict the response given the data. Decision trees are one of the easiest classification algorithms to understand and explain. In machine learning, classification has two stages, namely the learning stage and the prediction stage. In the learning phase, a model is built based on the given training data; in the prediction phase, the model is used to predict the response given the data. Decision trees are one of the easiest classification algorithms to understand and explain. Decision tree algorithm Decision tree algorithm is a kind of supervised learning algorithm. Unlike other supervised learning algorithms, the decision tree algorithm can be used to solve regression and classification problems.
2023-04-12 comment 01317
Course Introduction:可以通过使用性能分析工具分析Java函数的性能。具体步骤有:选择工具:内置工具(如System.nanoTime()、TimeUnit)或第三方工具(如JProfiler、YourKitProfiler、VisualVM)。实战案例:使用JProfiler分析斐波那契函数,重点关注方法调用树、CPU分析、内存分析和线程分析。优化:分析结果显示递归调用需要大量时间,采用记忆化技术优化性能。
2024-08-14 comment 0471
Course Introduction:The decision tree classifier is a machine learning algorithm based on a tree structure that is used to classify data. It establishes a tree-structured classification model by dividing the characteristics of the data. When there is new data that needs to be classified, the tree path is judged based on the feature values of the data, and the data is classified to the corresponding leaf nodes. When building a decision tree classifier, the data is generally divided recursively until a certain stopping condition is met. The construction process of a decision tree classifier can be divided into two main steps: feature selection and decision tree construction. Feature selection is an important step when building a decision tree. Its goal is to select the optimal features for partitioning as nodes to ensure that the data in each child node belongs to the same category as much as possible. Commonly used feature selection methods include
2024-01-22 comment 0179