leetcode中国ESTR2102-练习 这个存储库存储了我在香港中文大学（CUHK）课程 CSCI2100A/ESTR2102 数据结构的问题书中的一些问题的笔记和代码。 代码将从 2021 年 3 月 10 日起更新，希望每天都有一个问题。 请注意，作业中问题的所有解决方案将在迟到的截止日期之后发布。 所以请做好自己的工作。 提供了问题来源和相关的在线判断平台，以便您可以自行检查代码并查看其他示例解决方案。 如有任何疑问，请联系我。 欢迎您提出请求。 日期 问题 在线裁判 标签 完成 示例代码 3月10日 2.2 LSQ 3月11日 3.24 树 3月12日 3.25 树

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这是一本扫描版。以下是英文介绍。 Publication Date: August 14, 1995 | ISBN-10: 0201848406 | ISBN-13: 978-0201848403 | Edition: 2 The best-selling book on computer graphics is now available in this C-language version. All code has been converted into C, and changes through the ninth printing of the second edition have been incorporated. The book's many outstanding features continue to ensure its position as the standard computer graphics text and reference. By uniquely combining current concepts and practical applications in computer graphics, four well-known authors provide here the most comprehensive, authoritative, and up-to-date coverage of the field. The important algorithms in 2D and 3D graphics are detailed for easy implementation, including a close look at the more subtle special cases. There is also a thorough presentation of the mathematical principles of geometric transformations and viewing. In this book, the authors explore multiple perspectives on computer graphics: the user's, the application programmer's, the package implementor's, and the hardware designer's. For example, the issues of user-centered design are expertly addressed in three chapters on interaction techniques, dialogue design, and user interface software. Hardware concerns are examined in a chapter, contributed by Steven Molnar and Henry Fuchs, on advanced architectures for real-time, high performance graphics. The comprehensive topic coverage includes: *Programming with SRGP, a simple but powerful raster graphics package that combines features of Apple's QuickDraw and the MIT X Window System graphics library. *Hierarchical, geometric modeling using SPHIGS, a simplified dialect of the 3D graphics standard PHIGS. *Raster graphics hardware and software, including both basic and advanced algorithms for scan converting and clipping lines, polygons, conics, spline curves, and text. *Image synthesis, including visible-surface determination, illumination and shading models, image manipulation, and antialiasing. *Techniques for photorealistic rendering, including ray tracing and radiosity methods. *Surface modeling with parametric polynomials, including NURBS, and solid-modeling representations such as B-reps, CSG, and octrees. *Advanced modeling techniques such as fractals, grammar-based models, particle systems. *Concepts of computer animation and descriptions of state-of-the-art animation systems. Over 100 full-color plates and over 700 figures illustrate the techniques presented in the book. 0201848406B04062001

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Deep Learning, Vol. 2: From Basics to Practice By 作者: Andrew Glassner Pub Date: 2018 ISBN: n/a Pages: (914 of 1750) Format: PDF Publication Date: February 19, 2018 Language: English ASIN: B079Y1M81K People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Contents below. Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn’t in the programming. Rather, it’s knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today’s tools, those choices require understanding what’s going on under the hood. This book is designed to give you that understanding. You’ll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You’ll be able to read and understand the documentation for whatever library you’d like to use. And you’ll be able to follow exciting, on-going breakthroughs as they appear, because you’ll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning. The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use. You don’t need any previous experience with machine learning or deep learning for this book. You don’t need to be a mathematician, because there’s nothing in the book harder than the occasional multiplication. You don’t need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware. Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure. Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code. The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice. Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more. This friendly, informal book puts those tools into your pocket.

This is one of my most favorite books that teache deep learning from the very basic to advance level. The book does not emphasize on mathematics but explains the logic behind each deep learning algorithm in plain English. Even if you are the new guy in deep learning or you are already at an advance level, this book is for you. Vol 2 will be coming shortly

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