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...
Course Introduction:Graph neural networks (GNN) have made rapid and incredible progress in recent years. Graph neural network, also known as graph deep learning, graph representation learning (graph representation learning) or geometric deep learning, is the fastest growing research topic in the field of machine learning, especially deep learning. The title of this sharing is "Basics, Frontiers and Applications of GNN", which mainly introduces the general content of the comprehensive book "Basics, Frontiers and Applications of Graph Neural Networks" compiled by scholars Wu Lingfei, Cui Peng, Pei Jian and Zhao Liang. . 1. Introduction to graph neural networks 1. Why study graphs? Graphs are a universal language for describing and modeling complex systems. The graph itself is not complicated, it mainly consists of edges and nodes. We can use nodes to represent any object we want to model, and edges to represent two
2023-04-11 comment 0 874
Course Introduction:In 2005, the release of the epoch-making work "TheGraphNeuralNetworkModel" brought graph neural networks to everyone. Prior to this, the way scientists processed graph data was to convert the graph into a set of "vector representations" during the data preprocessing stage. The emergence of CNN has completely changed the disadvantages of this information loss. In the past 20 years, generations of models have continued to evolve, promoting progress in the ML field. Today, Google officially announced the release of TensorFlowGNN1.0 (TF-GNN) - a production-tested library for building GNNs on a large scale. It supports both modeling and training in TensorFlow and the extraction of input graphs from large data stores. TF-GNN is
2024-02-07 comment 0 230
Course Introduction:These powerful algorithms have gained tremendous interest over the past few years. However, this performance is based on the assumption of static graph structure, which limits the performance of graph neural networks when data changes over time. Sequential graph neural network is an extension of graph neural network that considers time factors. In recent years, various sequential graph neural network algorithms have been proposed and have achieved better performance than other deep learning algorithms in multiple time-related applications. This review discusses interesting topics related to spatiotemporal graph neural networks, including algorithms, applications, and open challenges. Paper address: https://arxiv.org/abs/2301.105691. Introduction Graph neural network (GNN) is a type of deep learning model specifically designed to process graph-structured data. These models
2023-04-13 comment 0 1335