Editor: Amusi | Source: Zhihu, originally from: cver https://www.zhihu.com/question/25097993 This article is for academic sharing only. If there is infringement, the article will be deleted. What Are the Techniques for Tuning Parameters in Deep Learning? The effectiveness of deep learning largely depends on how well the parameters are tuned. So how can we quickly and … Read more
Hello everyone, I am Dong Ge! A few days ago, Tongyi officially launched theTongyi Wanshang 2.1 series, significantly enhancing its image generation and video generation capabilities, with great improvements in UI optimization, Chinese character generation, complex camera movements, generation stability, and clarity. The New Version of Tongyi Wanshang Has Greatly Improved Capabilities Using the recommended … Read more
Yesterday, OpenAI released what I believe is the most exciting product since ChatGPT – ChatGPT Operator. Since getting access to my account last night, I have been testing various tasks and quickly documenting some thoughts on this product in this article: Operator and CUA Model – The Dawn of Agentic AI EraIn the previous public … Read more
Author: louwill Although deep learning is currently very popular, Boosting algorithms represented by XGBoost, LightGBM, and CatBoost still have a wide range of applications. Leaving aside the applications of deep learning suitable for unstructured data such as images, text, speech, and video, Boosting algorithms remain the first choice for structured data with fewer training samples. … Read more
Core Points:Complete Summary of XGBoost Core Issues! Hello, I am Cos Dazhuang! Today I will share content about XGBoost~ XGBoost is very important, especially excelling in classification, regression, and ranking problems. Its practical applications include financial risk control, medical diagnosis, industrial manufacturing, and advertising click-through rate prediction. With its efficient performance and robustness, XGBoost has … Read more
↑↑↑ Follow “Star Mark” Datawhale Daily Insights & Monthly Study Groups, don’t miss out Datawhale Insights Author: Wang Maolin, Huazhong University of Science and Technology, Datawhale Member Content Overview XGBoost Model and Parameter Tuning Summary XGBoost Principles XGBoost Advantages Summary XGBoost Parameter Details XGBoost Quick Usage XGBoost Parameter Tuning Methods PPT Download: Reply “210502” in … Read more
XGBoost has shone in Kaggle competitions. In previous articles, the principles of the XGBoost algorithm and the XGBoost splitting algorithm were introduced. Most explanations of XGBoost parameters online only scratch the surface, making it extremely unfriendly for those new to machine learning algorithms. This article will explain some important parameters while referring to mathematical formulas … Read more
XGBoost has shone in Kaggle competitions. In previous articles, the principles of the XGBoost algorithm and the XGBoost splitting algorithm were introduced. Most explanations of XGBoost parameters found online only scratch the surface, making it extremely unfriendly for those new to machine learning algorithms. This article will explain some important parameters while referencing mathematical formulas … Read more
Hello everyone, I am Lucy from the AI UP community. Welcome to join our technical group for discussions. Add WeChat: Fintech53, with your job title as a note. Today I want to talk about automated machine learning tools — AutoML tools. This is a very popular trend in the field of AI, and whether you … Read more
Author: Jeremy Jordan Translation by Machine Heart Contributors: Huang Xiaotian, Xu Di Every machine learning researcher faces the challenge of hyperparameter tuning, and during this tuning process, the adjustment of the learning rate is a crucial part. The learning rate represents the speed at which information accumulates over time in a neural network. Ideally, we … Read more