Course 2857
Course Introduction:Course introduction: 1. Cross-domain processing, token management, route interception; 2. Real interface debugging, API layer encapsulation; 3. Secondary encapsulation of Echarts and paging components; 4. Vue packaging optimization and answers to common problems.
Course 1795
Course Introduction:Apipost is an API R&D collaboration platform that integrates API design, API debugging, API documentation, and automated testing. It supports grpc, http, websocket, socketio, and socketjs type interface debugging, and supports privatized deployment. Before formally learning ApiPost, you must understand some related concepts, development models, and professional terminology. Apipost official website: https://www.apipost.cn
Course 5521
Course Introduction:(Consult WeChat: phpcn01) The comprehensive practical course aims to consolidate the learning results of the first two stages, achieve flexible application of front-end and PHP core knowledge points, complete your own projects through practical training, and provide guidance on online implementation. Comprehensive practical key practical courses include: social e-commerce system backend development, product management, payment/order management, customer management, distribution/coupon system design, the entire WeChat/Alipay payment process, Alibaba Cloud/Pagoda operation and maintenance, and project online operation. .....
Course 5172
Course Introduction:(Consult WeChat: phpcn01) Starting from scratch, you can solve conventional business logic, operate MySQL with PHP to add, delete, modify, and query, display dynamic website data, master the MVC framework, master the basics of the ThinkPHP6 framework, and learn and flexibly master all knowledge involved in PHP development. point.
Course 8713
Course Introduction:(Consult WeChat: phpcn01) The learning objectives of the front-end development part of the 22nd issue of PHP Chinese website: 1. HTML5/CSS3; 2. JavaScript/ES6; 3. Node basics; 4. Vue3 basics and advanced; 5. Mobile mall/ Website background homepage layout; 6. Automatic calculation of tabs/carousels/shopping carts...
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Course Introduction:Graph neural network is a type of neural network model designed to handle graph data analysis and graph data mining tasks. It can be used for multi-label classification tasks. In graph neural networks, nodes and edges can be represented as vectors or tensors, and the relationships between nodes and edges are learned through the computational process of the neural network. In order to improve the accuracy of the network's output results, the backpropagation algorithm can be used for optimization. Multi-label classification is a method of dividing data into several different categories, where each data point can belong to more than one category. In graph data analysis, multi-label classification can be used to identify labels of nodes or subgraphs, such as the interests and hobbies of users in social networks, the properties of chemical molecules, etc. The application of graph neural networks in multi-label classification tasks has broad application prospects. 1. The God of Pictures
2024-01-22 comment 374
Course Introduction:With the development of artificial intelligence technology, neural networks and deep neural networks have attracted more and more attention. They are used in fields such as computer vision, natural language processing, and machine translation. If you want to learn how to use PHP for neural network and deep neural network development, then this article will introduce you to some basic knowledge. Introduction to Neural Networks and Deep Neural Networks A neural network is a graphical model composed of nodes and edges. Each node represents a neuron, and each edge represents a connection between neurons. Neural networks can be used for classification, regression and clustering
2023-05-21 comment 0 573
Course Introduction:Translator | Reviewed by Zhu Xianzhong | Sun Shujuan Introduction Deep learning opens up a whole new world of possibilities for prediction on unstructured data. Nowadays, convolutional neural networks (CNN) are commonly used to process image data, while recurrent neural networks (RNN) are used to process text data, and so on. In the past few years, a new and exciting class of neural networks has emerged: Graph Neural Networks ("GNN" for short). As the name suggests, this network type focuses on processing graph data. In this post, you will learn the basics of how graph neural networks work and how to use the PytorchGeometric (PyG) library and OpenGraphBenchma
2023-04-19 comment 0 1331
Course Introduction:In recent years, neural networks and deep neural networks have become mainstream technologies in artificial intelligence and are widely used in image recognition, natural language processing, machine translation, recommendation systems and other fields. As a mainstream server-side programming language, PHP can also be applied to the implementation of neural networks and deep neural networks. This article will introduce how to use PHP to implement neural network and deep neural network models. 1. Neural Network Neural network is a computing model that imitates the biological nervous system and consists of multiple neurons interconnected. Neural Networks
2023-05-28 comment 0 647
Course Introduction:Quantum neural network is a new field that combines classical neural computing with quantum computing. It draws on the structure and function of the human brain, which processes information through interconnected "neurons." Different from traditional neural networks, quantum neural networks are usually hybrid, including classical pre-processing networks, quantum networks and classical post-processing algorithms. This combination can make full use of the advantages of quantum computing, such as parallel computing and quantum state superposition, thereby improving computing efficiency and performance. By combining classical and quantum computing, quantum neural networks have great potential for solving complex problems and optimizing tasks. The concept of a quantum neural network is to learn through classical preprocessing layers how to excite quantum circuits to produce the correct qubit behavior. Normally, this excitation causes the quantum state to
2024-01-24 comment 619