InterpretDL: Interpretation of Deep Learning Models，基于『飞桨』的模型可解释性算法库。

A multi-functional library for full-stack Deep Learning. Simplifies Model Building, API development, and Model Deployment.

What Has Been Enhanced in my Knowledge-Enhanced Language Model?

Official implementation of "HyPepTox-Fuse: An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors"

Integrating multimodal data through heterogeneous ensembles

Model Interpretability via Hierarchical Feature Perturbation

Streamlit dashboard frontend (user interface) to deploy a machine learning model to the web

AI Fairness and Explainability Toolkit (AFET) is an open-source project aimed at providing tools and frameworks to assess, visualize, and mitigate bias in machine learning models. It supports multiple ML frameworks and offers a comprehensive suite of metrics and visualization components to enhance model transparency and fairness.

Computer vision project for classifying American sign language.

API backend to deploy a machine learning model to the web

End-to-end machine learning project predicting wine quality based on chemical properties, featuring data exploration, feature engineering, model optimization, and an interactive Streamlit interface <<not compete yet>>