Machine Learning

Understanding XGBoost: Principles, Derivation, and Model Parameters

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Understanding XGBoost: Principles, Derivation, and Model Parameters

XGBoost is an integrated machine learning algorithm that can be used for various problems such as regression, classification, and ranking, and is widely used in machine learning competitions and industrial fields. Successful cases include: web text classification, customer behavior prediction, sentiment mining, ad click-through rate prediction, malware classification, item classification, risk assessment, and predicting dropout … Read more

XGBoost: Advanced Stock Price Prediction (With Code)

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XGBoost: Advanced Stock Price Prediction (With Code)

Star ★TopOfficial AccountLove you all♥ Author:Yibin Ng Translated by: 1+1=6 Recent Original Articles: ♥ 5 Machine Learning Algorithms for Stock Price Prediction (Code + Data) ♥ Two Sigma Uses News to Predict Stock Price Trends, Helping You Beat Kaggle ♥ 20,000 Words of Valuable Content: Using Cutting-Edge Deep Learning to Predict Stock Price Trends ♥ … Read more

My XGBoost Learning Experience and Hands-On Practice

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My XGBoost Learning Experience and Hands-On Practice

↑↑↑ Follow and “Star” Datawhale Daily Insights & Monthly Learning Teams, Don’t Miss Out Datawhale Insights Author: Li Zuxian, Shenzhen University, Datawhale University Group Member Zhihu Address: http://www.zhihu.com/people/meng-di-76-92 Today, I will mainly introduce XGBoost, one of the three giants in machine learning ensemble methods. This algorithm has previously shone in machine learning competitions and is … Read more

XGBoost Tutorial: A Comprehensive Guide

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XGBoost Tutorial: A Comprehensive Guide

Source: Machine Learning Algorithms This article is about 8400 words long and is recommended for a 10-minute read. This article provides a detailed explanation of the engineering application methods of XGBoost. The illustrated machine learning practical application demonstrates the application process and chain of machine learning algorithms in a case-driven and code-driven manner, mastering the … Read more

Unified Model for Controllable Multimodal Image Generation

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Unified Model for Controllable Multimodal Image Generation

Machine Heart Column Machine Heart Editorial Team Researchers from Salesforce AI, Northeastern University, and Stanford University proposed the MOE-style Adapter and Task-aware HyperNet to achieve multimodal conditional generation capabilities in UniControl. UniControl was trained on nine different C2I tasks, demonstrating strong visual generation capabilities and zero-shot generalization abilities. Paper link: https://arxiv.org/abs/2305.11147 Code link: https://github.com/salesforce/UniControl Project … Read more

Significant Advances in Multimodal Reinforcement Learning

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Significant Advances in Multimodal Reinforcement Learning

In 2024, significant progress has been made in the field of “multimodal + reinforcement learning”. Researchers have proposed various innovative methods to integrate data from different modalities to enhance the performance and applicability of reinforcement learning algorithms. For example, methods mentioned in the literature include utilizing Masked Multimodal Learning to achieve the fusion of visual … Read more

Deep Dive: How Generative AI Will Transform Supply Chain Management

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Deep Dive: How Generative AI Will Transform Supply Chain Management

In a famous article titled “The Death of Supply Chain Management” published in the Harvard Business Review in 2018, experts predicted that the function of supply chain management would be replaced by automated systems within 5-10 years. This bold prediction sparked widespread discussion at the time. The article mentioned:“The trend is clear: technology is replacing … Read more

CFGAN: A Collaborative Filtering Framework Based on Generative Adversarial Networks

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CFGAN: A Collaborative Filtering Framework Based on Generative Adversarial Networks

“ This article introduces the application of a Generative Adversarial Network in the recommendation field, namely CFGAN, along with its principles, potential issues, and solutions, and provides a code implementation and examples of running on public datasets.” Author Introduction: Zhang Xuxin, a master’s student at Huazhong University of Science and Technology, mainly researches data attack … Read more

Conditional GAN Network and Gene Expression Features for Hit Compound Discovery

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Conditional GAN Network and Gene Expression Features for Hit Compound Discovery

Author | Cheng Yu Reviewer | Zhu Yulei Today, I would like to introduce a paper jointly published in January 2020 by Bayer Crop Science, Bayer’s Machine Learning R&D Department, and the Genetic Toxicology Department in Nature Communications. This paper discusses a generative model for de novo design and synthetic optimization of molecules. The generative … Read more

Python Convolutional Neural Network (CNN) for Face Recognition

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Python Convolutional Neural Network (CNN) for Face Recognition

First, you need to install Python and find a user-friendly compiler, like RStudio. Next, you need to find the data. The original author has placed the data on Kaggle (https://www.kaggle.com/datasets/jessicali9530/lfw-dataset/code), but I have already downloaded it. Just reply with “Face Recognition” to get the complete data. Be sure to set the reading path to access … Read more