Tutorials, research notes, and practical guides on ML and multiscale modeling for fiber-based biomaterials. Home of FiberNet, a Python toolkit for fiber network design and optimization.
纤维生物·材料·结构的机器学习与多尺度模拟教程、研究笔记和实战指南。FiberNet 纤维网络设计 Python 工具包的官方网站。
A comprehensive three-platform guide covering virtual environments, Conda and Mamba, JupyterLab, and VS Code integration for materials science and biology graduate students.
A systematic guide to linear regression, Ridge, Lasso, polynomial regression, SVR, decision trees, random forests, and gradient boosting for scientific and engineering applications, with complete code examples.
A systematic introduction to deep learning: neurons, activation functions, backpropagation, regularization, and transfer learning, with practical guidance on when deep learning is the right tool in materials science.
A complete hands-on introduction to NetworkX: from graph theory fundamentals to network construction, analysis metrics, and visualization.
从虚拟环境原理到Conda/Mamba实战再到Jupyter与VS Code配置，三平台全覆盖的Python科学计算环境搭建指南，面向生物与材料科学研究生的从零到生产力完整教程。
常用的机器学习回归模型：从线性基准到梯度提升。面向生物与材料科学研究生的实用教程，含完整可运行代码示例与详细原理解析，适合零基础入门与进阶参考。
从神经元、激活函数、反向传播到正则化与迁移学习，系统讲解深度学习的核心概念与训练技巧，帮助材料科学和生物学研究生判断何时该用深度学习、何时传统机器学习方法更合适。
NetworkX 基础：用 Python 理解、构建与分析网络。面向生物与材料科学研究生的实用教程，含完整可运行代码示例与详细原理解析，适合零基础入门与进阶参考。