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en/docs/chapter_preface/about_the_book.md

@@ -1,4 +1,4 @@
-# About this book
+# About This Book
 
 This project aims to create an open-source, free, beginner-friendly introductory tutorial on data structures and algorithms.
 
@@ -6,7 +6,7 @@ This project aims to create an open-source, free, beginner-friendly introductory
 - 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.
 
-## Target audience
+## 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!
 
@@ -18,7 +18,7 @@ If you are an algorithm "expert," we look forward to receiving your valuable sug
 
     You need to have at least a programming foundation in any language, and be able to read and write simple code.
 
-## Content structure
+## Content Structure
 
 The main content of this book is shown in the figure below.
 
@@ -47,6 +47,6 @@ During the creation of this book, I received help from many people.
 
 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!
 
-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 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.
 
 **Heartfelt thanks to my parents, it is your support and encouragement that has given me the opportunity to do this interesting thing**.

en/docs/chapter_preface/suggestions.md

@@ -1,10 +1,10 @@
-# How to use this book
+# How to Use This Book
 
 !!! tip
 
     For the best reading experience, it is recommended that you read through this section.
 
-## Writing style conventions
+## 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.
@@ -191,7 +191,7 @@
     // comment
     ```
 
-## Learning efficiently with animated illustrations
+## 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.
 
@@ -199,7 +199,7 @@ If you find that a section of content provides animated illustrations as shown i
 
 ![Example of animated illustrations](../index.assets/animation.gif)
 
-## Deepening understanding through code practice
+## Deepening Understanding Through Code Practice
 
 The accompanying code for this book is hosted in the [GitHub repository](https://github.com/krahets/hello-algo). As shown in the figure below, **the source code comes with test cases and can be run with one click**.
 
@@ -231,7 +231,7 @@ In addition to running code locally, **the web version also supports visual runn
 
 ![Visual running of Python code](suggestions.assets/pythontutor_example.png)
 
-## Growing together through questions and discussions
+## 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.
 
@@ -239,7 +239,7 @@ As shown in the figure below, the web version has a comments section at the bott
 
 ![Example of comments section](../index.assets/comment.gif)
 
-## Algorithm learning roadmap
+## Algorithm Learning Roadmap
 
 From an overall perspective, we can divide the process of learning data structures and algorithms into three stages.
 

en/docs/chapter_preface/summary.md

@@ -1,5 +1,7 @@
 # Summary
 
+### 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 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.