Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool.

Direct3D入门参考资料《Introduction to 3D Game Programming with DirectX 10》中英文版

Programming skills are indispensable in today’s world, not just for computer science students, but also for anyone in any scientific or technical discipline. Introduction to Programming in Java, Second Edition, by Robert Sedgewick and Kevin Wayne is an accessible, interdisciplinary treatment that emphasizes important and engaging applications, not toy problems. The authors supply the tools needed for students and professionals to learn that programming is a natural, satisfying, and creative experience, and to become conversant with one of the world’s most widely used languages. This example-driven guide focuses on Java’s most useful features and brings programming to life for every student in the sciences, engineering, and computer science. Coverage includes Basic elements of programming: variables, assignment statements, built-in data types, conditionals, loops, arrays, and I/O, including graphics and sound Functions, modules, and libraries: organizing programs into components that can be independently debugged, maintained, and reused Algorithms and data structures: sort/search algorithms, stacks, queues, and symbol tables Applications from applied math, physics, chemistry, biology, and computer science Drawing on their extensive classroom experience, throughout the text the authors provide Q&As;, exercises, and opportunities for creative engagement with the material. Together with the companion materials described below, this book empowers people to pursue a modern approach to teaching and learning programming. Companion web site (introcs.cs.princeton.edu/java) contains Chapter summaries Supplementary exercises, some with solutions Detailed instructions for installing a Java programming environment Program code and test data suitable for easy download Detailed creative exercises, projects, and other supplementary materials Companion studio-produced online videos (informit.com/sedgewick) are available for purchase and provide students and professional

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一本描述机载雷达的好书，英文版，内容有趣，印刷非常清晰，引用了大量先进战机为例进行说明。中文版的翻译太差。

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Probability and distributions; multivariate distributions; distributions of functions of random variables; sampling distributions; limiting distributions; point estimation; confidence intervals; sufficient statistics; Bayesian estimation; maximum likelihood estimation; statistical tests.

An Introduction to Statistical Learning （英文版和中文版），统计学习导论 基于R应用，学习机器学习入门的经典书籍，包括中文版和英文版