build

en/docs/chapter_preface/about_the_book.md

@@ -6,28 +6,28 @@ comments: true
 
 This project aims to create an open-source, free, beginner-friendly introductory tutorial on data structures and algorithms.
 
-- The entire book uses animated illustrations, with clear and easy-to-understand content and a smooth learning curve, guiding beginners to explore the knowledge map of data structures and algorithms.
+- The entire book uses animated illustrations, with clear and easy-to-understand content and a smooth learning curve, guiding beginners through the landscape of data structures and algorithms.
 - The source code can be run with one click, helping readers improve their programming skills through practice and understand how algorithms work and the underlying implementation of data structures.
 - We encourage readers to learn from each other, and everyone is welcome to ask questions and share insights in the comments section, making progress together through discussion and exchange.
 
 ## 0.1.1   Target Audience
 
-If you are an algorithm beginner who has never been exposed to algorithms, or if you already have some problem-solving experience and have a vague understanding of data structures and algorithms, oscillating between knowing and not knowing, then this book is tailor-made for you!
+If you are an algorithm beginner who has never studied algorithms, or if you already have some problem-solving experience but only a hazy understanding of data structures and algorithms, then this book is tailor-made for you!
 
 If you have already accumulated a certain amount of problem-solving experience and are familiar with most question types, this book can help you review and organize your algorithm knowledge system, and the repository's source code can be used as a "problem-solving toolkit" or "algorithm dictionary."
 
-If you are an algorithm "expert," we look forward to receiving your valuable suggestions, or [participating in creation together](https://www.hello-algo.com/chapter_appendix/contribution/).
+If you are an algorithm "expert," we look forward to receiving your valuable suggestions, or [joining us as a contributor](https://www.hello-algo.com/chapter_appendix/contribution/).
 
 !!! success "Prerequisites"
 
-    You need to have at least a programming foundation in any language, and be able to read and write simple code.
+    You need basic programming knowledge in at least one language and the ability to read and write simple code.
 
 ## 0.1.2   Content Structure
 
 The main content of this book is shown in Figure 0-1.
 
 - **Complexity analysis**: Evaluation dimensions and methods for data structures and algorithms. Methods for calculating time complexity and space complexity, common types, examples, etc.
-- **Data structures**: Classification methods for basic data types and data structures. The definition, advantages and disadvantages, common operations, common types, typical applications, implementation methods, etc. of data structures such as arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs.
+- **Data structures**: Classification methods for basic data types and data structures. Definitions, advantages and disadvantages, common operations, common types, typical applications, implementation methods, and more for data structures such as arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs.
 - **Algorithms**: The definition, advantages and disadvantages, efficiency, application scenarios, problem-solving steps, and example problems of algorithms such as searching, sorting, divide and conquer, backtracking, dynamic programming, and greedy algorithms.
 
 ![Main content of this book](about_the_book.assets/hello_algo_mindmap.png){ class="animation-figure" }
@@ -36,25 +36,25 @@ The main content of this book is shown in Figure 0-1.
 
 ## 0.1.3   Acknowledgements
 
-This book has been continuously improved through the joint efforts of many contributors in the open-source community. Thanks to every contributor who invested time and effort, they are (in the order automatically generated by GitHub): krahets, coderonion, Gonglja, nuomi1, Reanon, justin-tse, hpstory, danielsss, curtishd, night-cruise, S-N-O-R-L-A-X, rongyi, msk397, gvenusleo, khoaxuantu, rivertwilight, K3v123, gyt95, zhuoqinyue, yuelinxin, Zuoxun, mingXta, Phoenix0415, FangYuan33, GN-Yu, longsizhuo, IsChristina, xBLACKICEx, guowei-gong, Cathay-Chen, pengchzn, QiLOL, magentaqin, hello-ikun, JoseHung, qualifier1024, thomasq0, sunshinesDL, L-Super, Guanngxu, Transmigration-zhou, WSL0809, Slone123c, lhxsm, yuan0221, what-is-me, Shyam-Chen, theNefelibatas, longranger2, codeberg-user, xiongsp, JeffersonHuang, prinpal, seven1240, Wonderdch, malone6, xiaomiusa87, gaofer, bluebean-cloud, a16su, SamJin98, hongyun-robot, nanlei, XiaChuerwu, yd-j, iron-irax, mgisr, steventimes, junminhong, heshuyue, danny900714, MolDuM, Nigh, Dr-XYZ, XC-Zero, reeswell, PXG-XPG, NI-SW, Horbin-Magician, Enlightenus, YangXuanyi, beatrix-chan, DullSword, xjr7670, jiaxianhua, qq909244296, iStig, boloboloda, hts0000, gledfish, wenjianmin, keshida, kilikilikid, lclc6, lwbaptx, linyejoe2, liuxjerry, llql1211, fbigm, echo1937, szu17dmy, dshlstarr, Yucao-cy, coderlef, czruby, bongbongbakudan, beintentional, ZongYangL, ZhongYuuu, ZhongGuanbin, hezhizhen, linzeyan, ZJKung, luluxia, xb534, ztkuaikuai, yw-1021, ElaBosak233, baagod, zhouLion, yishangzhang, yi427, yanedie, yabo083, weibk, wangwang105, th1nk3r-ing, tao363, 4yDX3906, syd168, sslmj2020, smilelsb, siqyka, selear, sdshaoda, Xi-Row, popozhu, nuquist19, noobcodemaker, XiaoK29, chadyi, lyl625760, lucaswangdev, 0130w, shanghai-Jerry, EJackYang, Javesun99, eltociear, lipusheng, KNChiu, BlindTerran, ShiMaRing, lovelock, FreddieLi, FloranceYeh, fanchenggang, gltianwen, goerll, nedchu, curly210102, CuB3y0nd, KraHsu, CarrotDLaw, youshaoXG, bubble9um, Asashishi, Asa0oo0o0o, fanenr, eagleanurag, akshiterate, 52coder, foursevenlove, KorsChen, GaochaoZhu, hopkings2008, yang-le, realwujing, Evilrabbit520, Umer-Jahangir, Turing-1024-Lee, Suremotoo, paoxiaomooo, Chieko-Seren, Allen-Scai, ymmmas, Risuntsy, Richard-Zhang1019, RafaelCaso, qingpeng9802, primexiao, Urbaner3, zhongfq, nidhoggfgg, MwumLi, CreatorMetaSky, martinx, ZnYang2018, hugtyftg, logan-qiu, psychelzh, Keynman, KeiichiKasai, and KawaiiAsh.
+This book has been continuously improved through the joint efforts of many contributors in the open-source community. Thanks to every contributor who invested time and effort, they are (in the order automatically generated by GitHub): krahets, coderonion, Gonglja, nuomi1, Reanon, justin-tse, hpstory, danielsss, curtishd, night-cruise, S-N-O-R-L-A-X, rongyi, msk397, gvenusleo, khoaxuantu, rivertwilight, K3v123, gyt95, zhuoqinyue, yuelinxin, Zuoxun, mingXta, Phoenix0415, FangYuan33, GN-Yu, longsizhuo, pengchzn, QiLOL, Cathay-Chen, guowei-gong, xBLACKICEx, IsChristina, JoseHung, qualifier1024, hello-ikun, magentaqin, Guanngxu, thomasq0, sunshinesDL, L-Super, Transmigration-zhou, WSL0809, Slone123c, lhxsm, yuan0221, what-is-me, theNefelibatas, Shyam-Chen, sangxiaai, longranger2, codeberg-user, xiongsp, JeffersonHuang, prinpal, seven1240, Wonderdch, malone6, xiaomiusa87, gaofer, bluebean-cloud, a16su, SamJin98, hongyun-robot, nanlei, XiaChuerwu, yd-j, iron-irax, mgisr, steventimes, junminhong, heshuyue, danny900714, Nigh, Dr-XYZ, MolDuM, XC-Zero, reeswell, PXG-XPG, NI-SW, Horbin-Magician, Enlightenus, YangXuanyi, xjr7670, beatrix-chan, DullSword, qq909244296, iStig, boloboloda, hts0000, gledfish, fbigm, echo1937, jiaxianhua, wenjianmin, keshida, kilikilikid, lclc6, lwbaptx, linyejoe2, liuxjerry, szu17dmy, dshlstarr, Yucao-cy, coderlef, czruby, bongbongbakudan, beintentional, ZongYangL, ZhongYuuu, ZhongGuanbin, hezhizhen, linzeyan, ZJKung, JTCPOWI, KawaiiAsh, luluxia, xb534, ztkuaikuai, yw-1021, ElaBosak233, baagod, zhouLion, yishangzhang, yi427, yanedie, yabo083, weibk, wangwang105, th1nk3r-ing, tao363, 4yDX3906, syd168, sslmj2020, smilelsb, siqyka, selear, sdshaoda, Xi-Row, popozhu, nuquist19, noobcodemaker, XiaoK29, chadyi, lyl625760, lucaswangdev, llql1211, 0130w, shanghai-Jerry, EJackYang, Javesun99, eltociear, lipusheng, KNChiu, BlindTerran, ShiMaRing, lovelock, FreddieLi, FloranceYeh, fanchenggang, gltianwen, goerll, nedchu, curly210102, CuB3y0nd, KraHsu, CarrotDLaw, youshaoXG, bubble9um, Asashishi, Asa0oo0o0o, fanenr, eagleanurag, akshiterate, 52coder, foursevenlove, KorsChen, hopkings2008, yang-le, realwujing, Evilrabbit520, Umer-Jahangir, Turing-1024-Lee, Suremotoo, paoxiaomooo, Chieko-Seren, Senrian, Allen-Scai, 19santosh99, ymmmas, Risuntsy, Richard-Zhang1019, RafaelCaso, qingpeng9802, primexiao, Urbaner3, codetypess, nidhoggfgg, MwumLi, CreatorMetaSky, martinx, ZnYang2018, hugtyftg, logan-qiu, psychelzh, Kunchen-Luo, Keynman, and KeiichiKasai.
 
-The code review work for this book was completed by coderonion, curtishd, Gonglja, gvenusleo, hpstory, justin-tse, khoaxuantu, krahets, night-cruise, nuomi1, Reanon and rongyi (in alphabetical order). Thanks to them for the time and effort they put in, it is they who ensure the standardization and unity of code in various languages.
+The code review work for this book was completed by coderonion, curtishd, Gonglja, gvenusleo, hpstory, justin-tse, khoaxuantu, krahets, night-cruise, nuomi1, Reanon and rongyi (in alphabetical order). Thanks to them for the time and effort they put in; they helped keep the code consistent and standardized across the different language versions.
 
-The Traditional Chinese version of this book was reviewed by Shyam-Chen and Dr-XYZ, the English version was reviewed by yuelinxin, K3v123, QiLOL, Phoenix0415, SamJin98, yanedie, RafaelCaso, pengchzn, thomasq0 and magentaqin, and the Japanese edition was reviewed by eltociear. It is because of their continuous contributions that this book can serve a wider readership, and we thank them.
+The English version of this book was reviewed by yuelinxin, K3v123, magentaqin, QiLOL, Phoenix0415, SamJin98, yanedie, RafaelCaso, pengchzn and thomasq0; the Japanese version was reviewed by eltociear; the Russian version was reviewed by И. А. Шевкун and Yuyan Huang; and the Traditional Chinese version was reviewed by Shyam-Chen and Dr-XYZ. Thanks to their contributions, this book is able to serve a broader readership, and we are deeply grateful to them.
 
 The ePub ebook generation tool for this book was developed by zhongfq. We thank him for his contribution, which provides readers with a more flexible way to read.
 
 During the creation of this book, I received help from many people.
 
 - Thanks to my mentor at the company, Dr. Li Xi, who encouraged me to "take action quickly" during a conversation, strengthening my determination to write this book;
-- Thanks to my girlfriend Bubble as the first reader of this book, who provided many valuable suggestions from the perspective of an algorithm beginner, making this book more suitable for novices to read;
+- Thanks to my girlfriend Bubble, the first reader of this book, who provided many valuable suggestions from the perspective of an algorithm beginner, making this book more approachable for beginners;
 - Thanks to Tengbao, Qibao, and Feibao for coming up with a creative name for this book, evoking everyone's fond memories of writing their first line of code "Hello World!";
 - Thanks to Xiaoquan for providing professional help in intellectual property rights, which played an important role in the improvement of this open-source book;
-- Thanks to Sutong for designing the beautiful cover and logo for this book, and for patiently making revisions multiple times driven by my obsessive-compulsive disorder;
-- Thanks to @squidfunk for the typesetting suggestions, as well as for developing the open-source documentation theme [Material-for-MkDocs](https://github.com/squidfunk/mkdocs-material/tree/master).
+- Thanks to Sutong for designing the beautiful cover and logo for this book, and for patiently revising them many times at my perfectionist insistence;
+- Thanks to @squidfunk for the typesetting suggestions, as well as for developing the open-source documentation theme [Material-for-MkDocs](https://github.com/squidfunk/mkdocs-material).
 
-During the writing process, I read many textbooks and articles on data structures and algorithms. These works provided excellent examples for this book and ensured the accuracy and quality of the book's content. I would like to thank all the teachers and predecessors for their outstanding contributions!
+During the writing process, I read many textbooks and articles on data structures and algorithms. These works served as excellent models for this book and helped ensure the accuracy and quality of its content. I would like to thank all the teachers and predecessors for their outstanding contributions!
 
-This book advocates a learning method that combines hands and brain, and in this regard I was deeply inspired by [Dive into Deep Learning](https://github.com/d2l-ai/d2l-zh). I highly recommend this excellent work to all readers.
+This book advocates a hands-on approach to learning, and in this respect I was deeply inspired by [Dive into Deep Learning](https://github.com/d2l-ai/d2l-zh). I highly recommend this excellent work to all readers.
 
-**Heartfelt thanks to my parents, it is your support and encouragement that has given me the opportunity to do this interesting thing**.
+**Heartfelt thanks to my parents. It is your support and encouragement that gave me the opportunity to pursue this enjoyable project**.

en/docs/chapter_preface/suggestions.md

@@ -10,12 +10,12 @@ comments: true
 
 ## 0.2.1   Writing Style Conventions
 
-- Titles marked with `*` are optional sections with relatively difficult content. If you have limited time, you can skip them first.
-- Technical terms will be in bold (in paper and PDF versions) or underlined (in web versions), such as <u>array</u>. It is recommended to memorize them for reading literature.
+- Sections marked with `*` after the title are optional and somewhat more challenging. If you're short on time, you can skip them on your first pass.
+- Technical terms are shown in bold (in the print and PDF editions) or underlined (in the web edition), such as <u>array</u>. They are worth remembering, as they will help when reading technical literature.
 - Key content and summary statements will be **bolded**, and such text deserves special attention.
 - Words and phrases with specific meanings will be marked with "quotation marks" to avoid ambiguity.
-- When it comes to nouns that are inconsistent between programming languages, this book uses Python as the standard, for example, using `None` to represent "null".
-- This book partially abandons the comment conventions of programming languages in favor of more compact content layout. Comments are mainly divided into three types: title comments, content comments, and multi-line comments.
+- When terminology differs across programming languages, this book follows Python conventions; for example, it uses `None` to represent "null".
+- This book partially relaxes conventional programming-language comment styles in favor of a more compact layout. Comments are mainly divided into three types: title comments, content comments, and multi-line comments.
 
 === "Python"
 
@@ -141,10 +141,8 @@ comments: true
 
     // Content comment, used to explain code in detail
 
-    /**
-     * Multi-line
-     * comment
-     */
+    // Multi-line
+    // comment
     ```
 
 === "C"
@@ -186,9 +184,9 @@ comments: true
 
 ## 0.2.2 &nbsp; Learning Efficiently with Animated Illustrations
 
-Compared to text, videos and images have higher information density and structural organization, making them easier to understand. In this book, **key and difficult knowledge will mainly be presented in the form of animated illustrations**, with text serving as explanation and supplement.
+Compared with plain text, videos and images have higher information density and a clearer structure, making them easier to understand. In this book, **key concepts and challenging topics are presented mainly through animated illustrations**, with text serving as explanation and supplement.
 
-If you find that a section of content provides animated illustrations as shown in Figure 0-2 while reading this book, **please focus on the illustrations first, with text as a supplement**, and combine the two to understand the content.
+If, while reading this book, you encounter an animated illustration like the one shown below, **treat the illustration as primary and the text as supplementary**, and use both together to understand the content.
 
 ![Example of animated illustrations](../index.assets/animation.gif){ class="animation-figure" }
 
@@ -200,15 +198,15 @@ The accompanying code for this book is hosted in the [GitHub repository](https:/
 
 If time permits, **it is recommended that you type out the code yourself**. If you have limited study time, please at least read through and run all the code.
 
-Compared to reading code, the process of writing code often brings more rewards. **Learning by doing is the real learning**.
+Compared with simply reading code, writing it yourself often brings greater rewards. **Hands-on practice is where real learning happens**.
 
 ![Example of running code](../index.assets/running_code.gif){ class="animation-figure" }
 
 <p align="center"> Figure 0-3 &nbsp; Example of running code </p>
 
-The preliminary work for running code is mainly divided into three steps.
+Getting the code running mainly involves three preliminary steps.
 
-**Step 1: Install the local programming environment**. Please follow the [tutorial](https://www.hello-algo.com/chapter_appendix/installation/) shown in the appendix for installation. If already installed, you can skip this step.
+**Step 1: Install the local programming environment**. Please follow the [tutorial](https://www.hello-algo.com/chapter_appendix/installation/) in the appendix. If it is already installed, you can skip this step.
 
 **Step 2: Clone or download the code repository**. Visit the [GitHub repository](https://github.com/krahets/hello-algo). If you have already installed [Git](https://git-scm.com/downloads), you can clone this repository with the following command:
 
@@ -216,7 +214,7 @@ The preliminary work for running code is mainly divided into three steps.
 git clone https://github.com/krahets/hello-algo.git
 ```
 
-Of course, you can also click the "Download ZIP" button at the location shown in Figure 0-4 to directly download the code compressed package, and then extract it locally.
+Alternatively, you can click the "Download ZIP" button shown below to download a ZIP archive of the repository directly and then extract it locally.
 
 ![Clone repository and download code](suggestions.assets/download_code.png){ class="animation-figure" }
 
@@ -228,7 +226,7 @@ Of course, you can also click the "Download ZIP" button at the location shown in
 
 <p align="center"> Figure 0-5 &nbsp; Code blocks and corresponding source code files </p>
 
-In addition to running code locally, **the web version also supports visual running of Python code** (implemented based on [pythontutor](https://pythontutor.com/)). As shown in Figure 0-6, you can click "Visual Run" below the code block to expand the view and observe the execution process of the algorithm code; you can also click "Full Screen View" for a better viewing experience.
+In addition to running code locally, **the web version also supports visual execution of Python code** (implemented based on [pythontutor](https://pythontutor.com/)). As shown in Figure 0-6, you can click "Visual Run" below the code block to expand the view and observe the execution process of the algorithm code; you can also click "Full Screen View" for a better viewing experience.
 
 ![Visual running of Python code](suggestions.assets/pythontutor_example.png){ class="animation-figure" }
 
@@ -236,9 +234,9 @@ In addition to running code locally, **the web version also supports visual runn
 
 ## 0.2.4 &nbsp; Growing Together Through Questions and Discussions
 
-When reading this book, please do not easily skip knowledge points that you have not learned well. **Feel free to ask your questions in the comments section**, and my friends and I will do our best to answer you, and generally reply within two days.
+When reading this book, please do not skip over points that you still do not fully understand. **Feel free to ask your questions in the comments section**, and my friends and I will do our best to answer them, usually within two days.
 
-As shown in Figure 0-7, the web version has a comments section at the bottom of each chapter. I hope you will pay more attention to the content of the comments section. On the one hand, you can learn about the problems that everyone encounters, thus checking for omissions and stimulating deeper thinking. On the other hand, I hope you can generously answer other friends' questions, share your insights, and help others progress.
+As shown in Figure 0-7, the web version has a comments section at the bottom of each chapter. I encourage you to pay close attention to the discussions there. On the one hand, you can learn about the problems that others encounter, thereby filling gaps in your own understanding and prompting deeper thought. On the other hand, I hope you will generously answer other readers' questions, share your insights, and help others improve.
 
 ![Example of comments section](../index.assets/comment.gif){ class="animation-figure" }
 
@@ -246,10 +244,10 @@ As shown in Figure 0-7, the web version has a comments section at the bottom of
 
 ## 0.2.5 &nbsp; Algorithm Learning Roadmap
 
-From an overall perspective, we can divide the process of learning data structures and algorithms into three stages.
+Overall, we can divide the process of learning data structures and algorithms into three stages.
 
 1. **Stage 1: Algorithm introduction**. We need to familiarize ourselves with the characteristics and usage of various data structures, and learn the principles, processes, uses, and efficiency of different algorithms.
-2. **Stage 2: Practice algorithm problems**. It is recommended to start with popular problems, and accumulate at least 100 problems first, to familiarize yourself with mainstream algorithm problems. When first practicing problems, "knowledge forgetting" may be a challenge, but rest assured, this is very normal. We can review problems according to the "Ebbinghaus forgetting curve", and usually after 3-5 rounds of repetition, we can firmly remember them. For recommended problem lists and practice plans, please see this [GitHub repository](https://github.com/krahets/LeetCode-Book).
+2. **Stage 2: Practice algorithm problems**. It is recommended to start with popular problems and solve at least 100 of them first, so that you become familiar with mainstream algorithm questions. When you first begin practicing problems, "knowledge forgetting" may feel like a challenge, but rest assured, this is very normal. We can review problems according to the "Ebbinghaus forgetting curve", and after 3-5 rounds of repetition, they usually stick firmly in memory. For recommended problem lists and practice plans, please see this [GitHub repository](https://github.com/krahets/LeetCode-Book).
 3. **Stage 3: Building a knowledge system**. In terms of learning, we can read algorithm column articles, problem-solving frameworks, and algorithm textbooks to continuously enrich our knowledge system. In terms of practicing problems, we can try advanced problem-solving strategies, such as categorization by topic, one problem multiple solutions, one solution multiple problems, etc. Related problem-solving insights can be found in various communities.
 
 As shown in Figure 0-8, the content of this book mainly covers "Stage 1", aiming to help you more efficiently carry out Stage 2 and Stage 3 learning.

en/docs/chapter_preface/summary.md

@@ -6,9 +6,9 @@ comments: true
 
 ### 1.   Key Review
 
-- The main audience of this book is algorithm beginners. If you already have a certain foundation, this book can help you systematically review algorithm knowledge, and the source code in the book can also be used as a "problem-solving toolkit."
+- The main audience of this book is algorithm beginners. If you already have some background, this book can help you systematically review algorithm knowledge, and the source code in the book can also be used as a "problem-solving toolkit."
 - The content of the book mainly includes three parts: complexity analysis, data structures, and algorithms, covering most topics in this field.
 - For algorithm novices, reading an introductory book during the initial learning stage is crucial, as it can help you avoid many detours.
-- The animated illustrations in the book are usually used to introduce key and difficult knowledge. When reading this book, you should pay more attention to these contents.
+- The animated illustrations in the book are usually used to introduce key concepts and challenging topics. When reading this book, you should pay more attention to these topics.
 - Practice is the best way to learn programming. It is strongly recommended to run the source code and type the code yourself.
 - The web version of this book has a comments section for each chapter, where you are welcome to share your questions and insights at any time.