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✔（已完结）最全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】
《Pytorch实用教程》（第二版）无论是零基础入门，还是CV、NLP、LLM项目应用，或是进阶工程化部署落地，在这里都有。相信在本书的帮助下，读者将能够轻松掌握 PyTorch 的使用，成为一名优秀的深度学习工程师。
深度学习100例、深度学习DL、图片分类、目标识别、目标检测、自然语言处理nlp、文本分类、TensorFlow、PyTorch
《深入浅出图神经网络：GNN原理解析》配套代码
《机器学习理论导引》（宝箱书）的证明、案例、概念补充与参考文献讲解。
Notes on Scientific Computing for Biomechanics and Motor Control
Parameterized Explainer for Graph Neural Network
图神经网络(图卷积网络) 个人学习总结
图神经网络、图卷积网络、图注意力网络、图自编码网络、时空图神经网络等论文合集。
SAGE-E source code and RoomGraph dataset for classifying room types for semantic enrichment BIM models.
Extract muscle synergies from experimental data obtained using Vicon Nexus using non-negative matrix factorisation.
Biomechanical modeling in Google Colab
Implemented a Physics-Informed Graph Neural Network (GNN) inspired by Google DeepMind’s research to simulate particle dynamics with high accuracy (96.2%). The model leverages PyTorch, GNNs, and tra…
100-Days-Of-ML-Code中文版
Open source implementation of "StructureGraph: A universal performance evaluation method for engineering structures via heterogeneous graph neural network"