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：We know that quantizing activations, weights and gradients into 4-bit is very valuable for speeding up neural network training. But existing 4-bit training methods require custom number formats that are not supported by contemporary hardware. In this article, Tsinghua Zhu Jun and others proposed a Transformer training method that uses the INT4 algorithm to implement all matrix multiplications. Whether the model is trained quickly or not is closely related to the requirements of activation values, weights, gradients and other factors. Neural network training requires a certain amount of calculation, and using low-precision algorithms (full quantization training or FQT training) is expected to improve computing and memory efficiency. FQT adds quantizers and dequantizers to the original full-precision computational graph and replaces expensive floating-point operations with cheap low-precision floating-point operations.

2023-07-02 comment 0466

Course Introduction：Highlights: Researchers propose a new technology called StableRep that uses images generated by artificial intelligence to train highly detailed artificial intelligence image models. StableRep is trained by using millions of labeled synthetic images, using "multiple "Positive Contrast Learning Method" to improve the learning process and apply it to the open source text-to-image model StableDiffusion-⚙️Although StableRep has achieved significant achievements in ImageNet classification, it is slow to generate images, and it is slow in both text prompts and generated images. There is a semantic mismatch between them. Webmaster’s Home (ChinaZ.com) News on November 28: Researchers from MIT and Google

2023-11-29 comment 0513

Course Introduction：How to implement distributed algorithms and model training in PHP microservices Introduction: With the rapid development of cloud computing and big data technology, the demand for data processing and model training is increasing. Distributed algorithms and model training are key to achieving efficiency, speed, and scalability. This article will introduce how to implement distributed algorithms and model training in PHP microservices, and provide some specific code examples. 1. What is distributed algorithm and model training? Distributed algorithm and model training is a technology that uses multiple machines or server resources to perform data processing and model training simultaneously.

2023-09-25 comment 0951

Course Introduction：Recently, diffusion models have surpassed GAN and autoregressive models and become the mainstream choice for generative models due to their excellent performance. Text-to-image generation models based on diffusion models (such as SD, SDXL, Midjourney, and Imagen) have demonstrated the amazing ability to generate high-quality images. Typically, these models are trained at a specific resolution to ensure efficient processing and accurate model training on existing hardware. Figure 1: Comparison of using different methods to generate 2048×2048 images under SDXL1.0. [1] In these diffusion models, pattern duplication and severe artifacts often occur. For example, it is shown on the far left side of Figure 1. These problems are particularly acute beyond the training resolution.

2024-04-08 comment975

Course Introduction：Paper link: https://arxiv.org/pdf/2207.09519.pdf Code link: https://github.com/gaopengcuhk/Tip-Adapter 1. Research Background Contrastive Image Language Pre-training (CLIP) model has recently demonstrated strong visual domain transfer capabilities and can perform zero-shot image recognition on a new downstream data set. In order to further improve the migration performance of CLIP, existing methods use few-shot settings, such as CoOp and CLIP-Adapter, which provide a small amount of training data for downstream data sets, making CLIP

2023-04-12 comment 0712