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Learn how to detect stereotypes in LLMs across embeddings, prompts, and generated text, with Python code, charts, and a ...

Learn how to apply Clean Architecture in Python without overengineering, using domain entities, use cases, Protocols, and lightweight infrastructure layers.

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This MATLAB and Simulink Challenge Project Hub contains a list of research and design project ideas. These projects will help you gain practical experience and insight into technology trends and ...

Existing models largely predict toxicity one end point at a time, although chemical toxicity emerges from coordinated perturbations across multiple targets, pathways, and adverse outcomes. We ...

Python implementation of TabNet, preprocessing ablation strategies, and tree-based machine learning models for landslide susceptibility assessment (LSA). The repository provides a reproducible ...

Given the class imbalance in the dataset, SMOTE (Synthetic Minority Over-sampling Technique) was applied to each training fold independently to balance the class distribution. Hyperparameters varied ...

在机器学习和人工智能项目中，数据质量直接决定模型效果上限。数据预处理作为建模前的关键工程，包含数据清洗、数据增强和数据标准化三大核心环节。数据清洗解决缺失值、异常值和单位混乱等脏数据问题；数据增强通过SMOTE重采样等技术应对样本不平衡；数据标准化则让特征尺度统一，保障 ...

我们用Python的 imblearn 库做SMOTE过采样，再用 sklearn 的Isolation Forest做无监督异常检测——它不需要标注故障类型，只要发现数据分布偏移就告警。 实测在风电齿轮箱监测中，比传统阈值告警提前47小时发现微裂纹。 模型层面 ：放弃Transformer，选择LSTM+Attention组合。