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Hands-On Credit Card Fraud Detection with Machine Learning: From XGBoost to Model Interpretability
An in-depth analysis of a complete open-source credit card fraud detection project, covering key technical points such as class imbalance handling, threshold optimization, SHAP interpretability, and time drift analysis.
银行客户流失预测平台实战：从SQL数据工程到Power BI可视化
解析一个端到端银行分析平台项目，涵盖客户细分、流失预测和留存策略优化，使用SQL、PostgreSQL、机器学习和Power BI构建完整的数据驱动解决方案。
Implementing Neural Networks from Scratch: Handwriting a Feedforward Network and Backpropagation with NumPy
An in-depth analysis of a neural network project implemented purely with NumPy, understanding the core mechanisms of forward propagation, backpropagation, and gradient descent, and building an intuitive understanding of the underlying principles of deep learning.
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大语言模型学习路线图：从基础理论到工程实践
探索一份系统化的大语言模型学习路径，涵盖从Transformer基础到LLM工程部署的完整知识体系，为希望深入AI领域的学习者提供参考。
AURONIS: An LLM-Powered Intelligent Code Review and Vulnerability Detection System
This article provides an in-depth analysis of the AURONIS project, exploring how it leverages large language models (LLMs) to enable real-time code scanning, multi-dimensional defect detection, and automated repair suggestions, offering new insights for improving code quality and development efficiency.
PhishGuard: An Intelligent Phishing Link Detection System Integrating Threat Intelligence and Machine Learning
This article provides an in-depth analysis of the PhishGuard project, exploring how it identifies malicious phishing URLs through multi-dimensional detection mechanisms—including threat intelligence queries, heuristic rule analysis, and machine learning models—to offer practical references for cybersecurity protection.
Building a Neural Network from Scratch: In-depth Analysis of MNIST Handwritten Digit Recognition with NumPy-Implemented MLP
This article provides an in-depth analysis of a pure NumPy-implemented Multilayer Perceptron (MLP) project, explaining the core principles of forward propagation, backpropagation, and gradient optimization to help readers understand the mathematical essence behind deep learning frameworks.
Schema-Miner: A Human-Machine Collaborative Scientific Schema Mining Framework Based on Large Language Models
Schema-Miner is an innovative open-source framework that leverages large language models (LLMs) and continuous human feedback to automate and enhance scientific schema mining tasks. This tool uses a three-stage iterative process to extract and organize scientific attributes from unstructured text, anchors schema elements to formal ontologies (such as QUDT), and provides a new method for structured knowledge representation in scientific research.
IPL Prediction and Analysis Platform: Reshaping Cricket Data Analysis with Machine Learning
A full-stack SaaS platform built with Next.js and Python machine learning tech stack, transforming Indian Premier League (IPL) historical data into actionable intelligent insights, including real-time statistics, player intelligent analysis, venue insights, and AI-driven match prediction features.
Investment Risk Assessment Platform: A Machine Learning-Powered Intelligent Financial Decision Tool
Introduces an AI investment risk assessment platform based on machine learning and financial indicators, discussing how to use data science methods to quantify investment risks and assist investment decisions.
Telecom Customer Churn Prediction Model: User Retention Analysis Based on Machine Learning
Introduces a machine learning model for customer churn prediction in the Nigerian telecom market, exploring how to identify high-risk churn users and develop retention strategies through data analysis.
Speech Emotion Recognition: A Machine Learning-Based Audio Emotion Analysis System
This article introduces a speech emotion recognition system built using the RAVDESS dataset and audio features such as MFCC and Mel spectrograms, discussing the application of audio feature extraction and machine learning in emotion recognition.
Heart Failure Prediction System: Real-Time Cardiac Risk Assessment Based on XGBoost
This article introduces a heart disease prediction system combining the XGBoost machine learning model and Streamlit interface to enable real-time heart failure risk assessment based on clinical parameters.
NLP-based Intelligent FAQ Chatbot: Application of Natural Language Processing in Customer Service
This article introduces an FAQ chatbot project based on natural language processing (NLP) technology, exploring how NLP enables intelligent Q&A and enhances customer service experience.
Pulsar-Net: A Physics-Inspired Machine Learning System for Automatic Pulsar Identification
This article introduces Pulsar-Net, an astronomical machine learning project that combines feature engineering, XGBoost, threshold optimization, and SHAP interpretability analysis to automatically identify real pulsars from radio survey data.
Application of Random Forest in Spatial Prediction and Environmental Modeling: From Theory to Practice
An in-depth analysis of the application of the random forest algorithm in spatial prediction and environmental modeling, exploring its principles, advantages, and practical application scenarios, providing a practical guide for geospatial data analysis.
LGLD-Net: A Breakthrough in Lightweight Global-Local Dual-Branch Neural Network for Medical Microscopic Image Classification
A lightweight neural network with only 161K parameters. By fusing local texture features and global context information, it achieves accuracy rates of 99.69% and 96.50% in acute lymphoblastic leukemia and blood cell classification tasks respectively, and can perform real-time inference at 17.56 FPS on Raspberry Pi 5.
Intelligent Crop Recommendation System: Machine Learning Application Based on Soil and Meteorological Data
A machine learning project developed using Python that recommends the most suitable crops for farmers by analyzing soil nutrients, pH values, and weather conditions, supporting the development of precision agriculture and sustainable agriculture.
Building a Neural Network from Scratch: Predicting Nifty 50 Volatility Using Pure NumPy
A neural network project implemented entirely from scratch using NumPy, including manually derived forward propagation, backpropagation, mini-batch gradient descent, and momentum optimization, applied to the task of predicting the actual volatility of India's Nifty 50 index.
SIRS Model for Generative AI Data Contamination: MATLAB Implementation and Numerical Analysis
A MATLAB project that uses the SIRS (Susceptible-Infected-Recovered-Susceptible) epidemiological model framework to study generative AI data contamination. It provides complete code implementation and numerical datasets, offering a mathematical modeling perspective to understand the impact of AI-generated content on the data ecosystem.
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