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...
Speeding up the ViteJs development model: Vue 3
2023-10-31 13:42:49 0 1 291
Why does height 100% work without DOCTYPE declaration?
2023-10-26 10:39:42 1 2 209
Execute multiple MYSQL queries using PHP
2023-10-25 11:41:44 0 1 238
Explained: Understanding mod_rewrite, URL rewriting, and creating "pretty links"
2023-10-20 15:47:10 0 2 323
Course Introduction:Gradient descent is a commonly used optimization algorithm and is widely used in machine learning. Python is a great programming language for data science, and there are many ready-made libraries for implementing gradient descent algorithms. This article will introduce the gradient descent algorithm in Python in detail, including concepts and implementation. 1. Definition of Gradient Descent Gradient descent is an iterative algorithm used to optimize the parameters of a function. In machine learning, we usually use gradient descent to minimize the loss function. Therefore, gradient descent can
2023-06-10 comment 0 1796
Course Introduction:The stochastic gradient descent algorithm is one of the commonly used optimization algorithms in machine learning. It is an optimized version of the gradient descent algorithm and can converge to the global optimal solution faster. This article will introduce the stochastic gradient descent algorithm in Python in detail, including its principles, application scenarios and code examples. 1. Principle of Stochastic Gradient Descent Algorithm Gradient Descent Algorithm Before introducing the stochastic gradient descent algorithm, let’s briefly introduce the gradient descent algorithm. The gradient descent algorithm is one of the commonly used optimization algorithms in machine learning. Its idea is to follow the negative gradient of the loss function.
2023-06-10 comment 0 1178
Course Introduction:What is the gradient descent algorithm in Python? The gradient descent algorithm is a commonly used mathematical optimization technique used to find the minimum value of a function. The algorithm gradually updates the parameter values of the function in an iterative manner, moving it toward the local minimum. In Python, the gradient descent algorithm is widely used in fields such as machine learning, deep learning, data science, and numerical optimization. Principle of Gradient Descent Algorithm The basic principle of gradient descent algorithm is to update along the negative gradient direction of the objective function. On a two-dimensional plane, the objective function can be
2023-06-04 comment 0 565
Course Introduction:Gradient descent is a commonly used optimization algorithm, mainly used in machine learning and deep learning to find the best model parameters or weights. Its core goal is to measure the difference between the model's predicted output and its actual output by minimizing a cost function. The algorithm uses the direction of steepest descent of the cost function gradient by iteratively adjusting the model parameters until it reaches the minimum value. Gradient calculation is implemented by taking the partial derivative of the cost function for each parameter. In gradient descent, each iteration the algorithm chooses an appropriate step size based on the learning rate, taking a step toward the steepest cost function. The choice of learning rate is very important because it affects the step size of each iteration and needs to be adjusted carefully to ensure that the algorithm can converge to the optimal solution. Practical Use Cases of Gradient DescentGradient Descent
2024-01-23 comment 0 542
Course Introduction:How to implement gradient descent algorithm using Python? The gradient descent algorithm is a commonly used optimization algorithm that is widely used in machine learning and deep learning. The basic idea is to find the minimum point of the function through iteration, that is, to find the parameter value that minimizes the function error. In this article, we will learn how to implement the gradient descent algorithm in Python and give specific code examples. The core idea of the gradient descent algorithm is to iteratively optimize along the opposite direction of the function gradient, thereby gradually approaching the minimum point of the function. in reality
2023-09-19 comment 0 971