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Hello 算法
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0.1. 关于本书
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0. 写在前面
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0. 写在前面
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0.1. 关于本书
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0.1. 关于本书
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目录
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0.1.1. 读者对象
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0.1.2. 内容结构
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复杂度分析
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数据结构
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算法
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0.1.3. 本书定位
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0.1.4. 本书特点
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0.1.5. 致谢
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0.2. 如何使用本书
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0.3. 编程环境安装
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0.4. 一起参与创作
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1. 引言
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1. 引言
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1.1. 算法无处不在
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1.2. 算法是什么
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2. 计算复杂度
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2. 计算复杂度
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2.1. 算法效率评估
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2.2. 时间复杂度
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< a href = "../../chapter_computational_complexity/space_complexity/" class = "md-nav__link" >
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2.3. 空间复杂度
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< a href = "../../chapter_computational_complexity/space_time_tradeoff/" class = "md-nav__link" >
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2.4. 权衡时间与空间
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< a href = "../../chapter_computational_complexity/summary/" class = "md-nav__link" >
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2.5. 小结
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3. 数据结构简介
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3. 数据结构简介
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3.1. 数据与内存
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3.2. 数据结构分类
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3.3. 小结
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4. 数组与链表
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4. 数组与链表
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4.1. 数组（ ）
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< a href = "../../chapter_array_and_linkedlist/linked_list/" class = "md-nav__link" >
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4.2. 链表（ ）
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< a href = "../../chapter_array_and_linkedlist/list/" class = "md-nav__link" >
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4.3. 列表（ ）
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4.4. 小结
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5. 栈与队列
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5. 栈与队列
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5.1. 栈（ ）
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5.2. 队列（ ）
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5.3. 双向队列（ ）
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5.4. 小结
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6. 散列表
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6. 散列表
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6.1. 哈希表（ ）
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< a href = "../../chapter_hashing/hash_collision/" class = "md-nav__link" >
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6.2. 哈希冲突处理
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6.3. 小结
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7. 二叉树
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7. 二叉树
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7.1. 二叉树（ ）
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7.2. 二叉树遍历
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7.3. 二叉搜索树
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7.4. AVL 树 *
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7.5. 小结
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8. 堆
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8. 堆
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8.1. 堆（ ）
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9. 图
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9. 图
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9.1. 图（ ）
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9.2. 图基础操作
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9.3. 图的遍历
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10. 查找算法
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10. 查找算法
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10.1. 线性查找
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10.2. 二分查找
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10.3. 哈希查找
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10.4. 小结
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11. 排序算法
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11. 排序算法
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11.1. 排序简介
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11.2. 冒泡排序
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11.3. 插入排序
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11.4. 快速排序
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< a href = "../../chapter_sorting/merge_sort/" class = "md-nav__link" >
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11.5. 归并排序
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11.6. 小结
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参考文献
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0.1.1. 读者对象
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0.1.2. 内容结构
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复杂度分析
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数据结构
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算法
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0.1.3. 本书定位
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0.1.4. 本书特点
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0.1.5. 致谢
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< h1 id = "01" > 0.1. 关于本书< a class = "headerlink" href = "#01" title = "Permanent link" > ¶ < / a > < / h1 >
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< div class = "admonition tip" >
< p class = "admonition-title" > Tip< / p >
< p > 对于本章内容，建议通读< a href = "https://www.hello-algo.com/chapter_preface/suggestions/" > 如何使用本书< / a > ，其余章节可根据需求选读。< / p >
< / div >
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< p > 五年前发生的一件事，成为了我职业生涯的重要转折点。当时的我在交大读研，对互联网求职一无所知，但仍然硬着头皮申请了 Microsoft 软件工程师实习。面试官让我在白板上写出“快速排序”代码，我畏畏缩缩地写了一个“冒泡排序”，并且还写错了< code > (ToT)< / code > 。从面试官的表情上，我看到了一个大大的 "GG" 。< / p >
< p > 此次失利倒逼我开始刷算法题。我采用“扫雷游戏”式的学习方法，两眼一抹黑刷题，扫到不会的“雷”就通过查资料把它“排掉”，配合周期性总结，逐渐形成了数据结构与算法的知识图景。幸运地，我在秋招斩获了多家大厂的 Offer 。< / p >
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< p > 回想自己当初在“扫雷式”刷题中被炸的满头包的痛苦，思考良久，< strong > 我意识到一本“前期刷题必看”的读物可以使算法小白少走许多弯路< / strong > 。写作意愿滚滚袭来，那就动笔吧：< / p >
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< h4 align = "center" > Hello， ！ < / h4 >
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< h2 id = "011" > 0.1.1. 读者对象< a class = "headerlink" href = "#011" title = "Permanent link" > ¶ < / a > < / h2 >
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< p > 如果您是「算法初学者」，完全没有接触过算法，或者已经有少量刷题，对数据结构与算法有朦胧的理解，在会与不会之间反复横跳，那么这本书就是为您而写！本书能够带来：< / p >
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< ul >
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< li > 带领你完整学习数据结构与算法，初步搭建知识框架，使刷题事半功倍；< / li >
< li > 提供写法标准整洁、可一键运行的配套代码，帮助你养成良好的编程习惯；< / li >
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< / ul >
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< p > 如果您是「算法老手」，已经积累一定刷题量，接触过大多数题型，那么本书内容对你来说可能稍显基础，但仍能够带来以下价值：< / p >
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< ul >
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< li > 本书篇幅不长，并且包含较多对比性和总结性的内容，可以帮助你回顾与梳理算法知识体系；< / li >
< li > 源代码实现了各种经典数据结构和算法，可以作为“刷题工具库”或“算法实现字典”来使用；< / li >
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< / ul >
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< p > 如果您是「算法大佬」，请受我膜拜！希望您可以抽时间提出意见建议，或者< a href = "https://www.hello-algo.com/chapter_preface/contribution/" > 一起参与创作< / a > ，帮助各位同学获取更好的学习内容，感谢！< / p >
< div class = "admonition success" >
< p class = "admonition-title" > 前置条件< / p >
< p > 您需要至少具备任一语言的编程基础，能够阅读和编写简单代码。< / p >
< / div >
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< h2 id = "012" > 0.1.2. 内容结构< a class = "headerlink" href = "#012" title = "Permanent link" > ¶ < / a > < / h2 >
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< p > 本书主要内容分为复杂度分析、数据结构、算法三个部分。< / p >
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< p > < img alt = "Hello 算法内容结构" src = "../about_the_book.assets/hello_algo_mindmap.png" / > < / p >
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< h3 id = "_1" > 复杂度分析< a class = "headerlink" href = "#_1" title = "Permanent link" > ¶ < / a > < / h3 >
< p > 首先介绍数据结构与算法的评价维度、算法效率的评估方法，引出了计算复杂度概念。< / p >
< p > 接下来，从 < strong > 函数渐近上界< / strong > 入手，分别介绍了 < strong > 时间复杂度< / strong > 和 < strong > 空间复杂度< / strong > ，包括推算方法、常见类型、示例等。同时，剖析了 < strong > 最差、最佳、平均< / strong > 时间复杂度的联系与区别。< / p >
< h3 id = "_2" > 数据结构< a class = "headerlink" href = "#_2" title = "Permanent link" > ¶ < / a > < / h3 >
< p > 首先介绍了常用的 < strong > 基本数据类型< / strong > 、以及它们是如何在内存中存储的。< / p >
< p > 接下来，介绍了两种 < strong > 数据结构分类方法< / strong > ，包括逻辑结构与物理结构。< / p >
< p > 后续展开介绍了 < strong > 数组、链表、栈、队列、散列表、树、堆、图< / strong > 等数据结构，关心以下内容：< / p >
< ul >
< li > 基本定义：数据结构的设计来源、存在意义；< / li >
< li > 主要特点：在各项数据操作中的优势、劣势；< / li >
< li > 常用操作：例如访问、更新、插入、删除、遍历、搜索等；< / li >
< li > 常见类型：在算法题或工程实际中，经常碰到的数据结构类型；< / li >
< li > 典型应用：此数据结构经常搭配哪些算法使用；< / li >
< li > 实现方法：对于重要的数据结构，将给出完整的实现示例；< / li >
< / ul >
< h3 id = "_3" > 算法< a class = "headerlink" href = "#_3" title = "Permanent link" > ¶ < / a > < / h3 >
< p > 包括 < strong > 查找算法、排序算法、搜索与回溯、动态规划、分治算法< / strong > ，内容包括：< / p >
< ul >
< li > 基本定义：算法的设计思想；< / li >
< li > 主要特点：使用前置条件、优势和劣势；< / li >
< li > 算法效率：最差和平均时间复杂度、空间复杂度；< / li >
< li > 实现方法：完整的算法实现，以及优化措施；< / li >
< li > 示例题目：结合例题加深理解；< / li >
< / ul >
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< h2 id = "013" > 0.1.3. 本书定位< a class = "headerlink" href = "#013" title = "Permanent link" > ¶ < / a > < / h2 >
< p > 总体上看，我认为可将学习数据结构与算法的过程分为三个阶段。< / p >
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< ul >
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< li > < strong > 第一阶段，算法入门< / strong > 。熟悉各种数据结构的特点、用法，学习各种算法的原理、流程、用途、效率等。< / li >
< li > < strong > 第二阶段，刷算法题< / strong > 。可以先从热门题单开刷，推荐< a href = "https://leetcode.cn/problem-list/xb9nqhhg/" > 剑指 Offer< / a > 、< a href = "https://leetcode.cn/problem-list/2cktkvj/" > LeetCode Hot 100< / a > ，先积累至少 100 道题量，熟悉大多数的算法问题。刚开始刷题时，“遗忘”是最大的困扰点，但这是很正常的，请不要担心。学习中有一种概念叫“周期性回顾”，同一道题隔段时间做一次，在重复 3 轮以上后，往往就能牢记于心了。< / li >
< li > < strong > 第三阶段，搭建知识体系< / strong > 。在学习方面，可以阅读算法专栏文章、解题框架、算法教材，不断地丰富知识体系。在刷题方面，可以开始采用进阶刷题方案，例如按专题分类、一题多解、一解多题等，相应刷题计划与心得可以在社区中找到，在此不做赘述。< / li >
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< / ul >
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< p > 根据观察，很多同学都是从“第二阶段”开始学习算法的。而作为入门教程，< strong > 本书内容主要对应“第一阶段”< / strong > ，致力于帮助读者更高效地开展第二、三阶段的学习。< / p >
< p > < img alt = "算法学习路线" src = "../suggestions.assets/learning_route.png" / > < / p >
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< h2 id = "014" > 0.1.4. 本书特点< a class = "headerlink" href = "#014" title = "Permanent link" > ¶ < / a > < / h2 >
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< p > < strong > 以实践为主< / strong > 。我们知道，学习英语期间光啃书本是远远不够的，需要多听、多说、多写，在实践中培养语感、积累经验。编程语言也是一门语言，因此学习方法也应是类似的，需要多看优秀代码、多敲键盘、多思考代码逻辑。< / p >
< p > 本书的理论部分占少量篇幅，主要分为两类：一是基础且必要的概念知识，以培养读者对于算法的感性认识；二是重要的分类、对比或总结，这是为了帮助你站在更高视角俯瞰各个知识点，形成连点成面的效果。< / p >
< p > 实践部分主要由示例和代码组成。代码配有简要注释，复杂示例会尽可能地使用视觉化的形式呈现。我强烈建议读者对照着代码自己敲一遍，如果时间有限，也至少逐行读、复制并运行一遍，配合着讲解将代码吃透。< / p >
< p > < strong > 视觉化学习< / strong > 。信息时代以来，视觉化的脚步从未停止。媒体形式经历了文字短信、图文 Email 、动图、短（长）视频、交互式 Web 、3D 游戏等演变过程， ， < / p >
< p > 近两年，短视频成为最受欢迎的信息媒介，可以在短时间内将高密度的信息“灌”给我们，有着极其舒适的观看体验。阅读则不然，读者与书本之间天然存在一种“疏离感”，我们看书会累、会走神、会停下来想其他事、会划下喜欢的句子、会思考某一片段的含义，这种疏离感给了读者与书本之间对话的可能，拓宽了想象空间。< / p >
< p > 本书作为一本入门教材，希望可以保有书本的“慢节奏”，但也会避免与读者产生过多“疏离感”，而是努力将知识完整清晰地推送到你聪明的小脑袋瓜中。我将采用视觉化的方式（例如配图、动画），尽我可能清晰易懂地讲解复杂概念和抽象示例。< / p >
< p > < strong > 内容精简化< / strong > 。大多数的经典教科书，会把每个主题都讲的很透彻。虽然透彻性正是其获得读者青睐的原因，但对于想要快速入门的初学者来说，这些教材的实用性不足。本书会避免引入非必要的概念、名词、定义等，也避免展开不必要的理论分析，毕竟这不是一本真正意义上的教材，主要任务是尽快地带领读者入门。< / p >
< p > 引入一些生活案例或趣味内容，非常适合作为知识点的引子或者解释的补充，但当融入过多额外元素时，内容会稍显冗长，也许反而使读者容易迷失、抓不住重点，这也是本书需要避免的。< / p >
< p > 敲代码如同写字，“美”是统一的追求。本书力求美观的代码，保证规范的变量命名、统一的空格与换行、对齐的缩进、整齐的注释等。< / p >
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< h2 id = "015" > 0.1.5. 致谢< a class = "headerlink" href = "#015" title = "Permanent link" > ¶ < / a > < / h2 >
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< p > 本书的成书过程中，我获得了许多人的帮助，包括但不限于：< / p >
< ul >
< li > 感谢我的女朋友泡泡担任本书的首位读者，从算法小白的视角为本书的写作提出了许多建议，使这本书更加适合算法初学者来阅读。< / li >
< li > 感谢腾宝、琦宝、飞宝为本书起了个响当当的名字，好听又有梗，直接唤起我最初敲下第一行代码 "Hello, World!" 的回忆。< / li >
2023-02-11 22:58:29 +08:00
< li > 感谢我的导师李博，在小酌畅谈时您告诉我“觉得应该做就去做”，坚定了我写这本书的决心。< / li >
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< li > 感谢苏潼为本书设计了封面和 LOGO ，我有些强迫症，前后多次修改，谢谢你的耐心。< / li >
2023-02-11 22:58:29 +08:00
< li > 感谢 @squidfunk 给出的写作排版建议，以及优秀开源项目 < a href = "https://github.com/squidfunk/mkdocs-material/tree/master" > Material-for-MkDocs< / a > 。< / li >
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< / ul >
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< p > 本书鼓励“手脑并用”的学习方式，在这点上受到了《动手学深度学习》很大影响，也在此向各位同学强烈推荐这本著作，包括< a href = "https://github.com/d2l-ai/d2l-zh" > 中文版< / a > 、< a href = "https://github.com/d2l-ai/d2l-en" > 英文版< / a > 、< a href = "https://space.bilibili.com/1567748478" > 李沐老师 bilibili 主页< / a > 。< / p >
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< p > 在写作过程中，我阅读了许多数据结构与算法的教材与文章，这些著作为本书作出了很好的榜样，保证了本书内容的正确性与质量，感谢前辈们的精彩创作！< / p >
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< p > 感谢父母，你们一贯的支持与鼓励给了我自由度来做这些有趣的事。< / p >
< h2 id = "__comments" > 评论< / h2 >
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