* Translate 1.0.0b6 release with the machine learning translator. * Update Dockerfile A few translation improvements. * Fix a badge logo. * Fix EN translation of chapter_appendix/terminology.md (#913) * Update README.md * Update README.md * translation: Refined the automated translation of README (#932) * refined the automated translation of README * Update index.md * Update mkdocs-en.yml --------- Co-authored-by: Yudong Jin <krahets@163.com> * translate: Embellish chapter_computational_complexity/index.md (#940) * translation: Update chapter_computational_complexity/performance_evaluation.md (#943) * Update performance_evaluation.md * Update performance_evaluation.md * Update performance_evaluation.md change 'methods' to 'approaches' on line 15 * Update performance_evaluation.md on line 21, change the sentence to 'the results could be the opposite on another computer with different specifications.' * Update performance_evaluation.md delete two short sentence on line 5 and 6 * Update performance_evaluation.md change `unavoidable` to `inevitable` on line 48 * Update performance_evaluation.md small changes on line 23 * translation: Update terminology and improve readability in preface summary (#954) * Update terminology and improve readability in preface summary This commit made a few adjustments in the 'summary.md' file for clearer and more accessible language. "Brushing tool library" was replaced with "Coding Toolkit" to better reflect common terminology. Also, advice for beginners in algorithm learning journey was reformulated to imply a more positive approach avoiding detours and common pitfalls. The section related to the discussion forum was rewritten to sound more inviting to readers. * Format * Optimize the translation of chapter_introduction/algorithms_are_everywhere. * Add .gitignore to Java subfolder. * Update the button assets. * Fix the callout * translation: chapter_computational_complexity/summary to en (#953) * translate chapter_computational_complexity/summary * minor format * Update summary.md with comment * Update summary.md * Update summary.md * translation: chapter_introduction/what_is_dsa.md (#962) * Optimize translation of what_is_dsa.md * Update * translation: chapter_introduction/summary.md (#963) * Translate chapter_introduction/summary.md * Update * translation: Update README.md (#964) * Update en translation of README.md * Update README.md * translation: update space_complexity.md (#970) * update space_complexity.md * the rest of translation piece * Update space_complexity.md --------- Co-authored-by: ThomasQiu <thomas.qiu@mnfgroup.limited> Co-authored-by: Yudong Jin <krahets@163.com> * translation: Update chapter_introduction/index.md (#971) * Update index.md sorry, first time doing this... now this is the final change. changes: title of the chapter is shorter. refined the abstract. * Update index.md --------- Co-authored-by: Yudong Jin <krahets@163.com> * translation: Update chapter_data_structure/classification_of_data_structure.md (#980) * update classification_of_data_structure.md * Update classification_of_data_structure.md --------- Co-authored-by: Yudong Jin <krahets@163.com> * translation: Update chapter_introduction/algorithms_are_everywhere.md (#972) * Update algorithms_are_everywhere.md changed or refined parts of the words and sentences including tips. Some of them I didnt change that much because im worried that it might not meet the requirement of accuracy. some other ones i changed a lot to make it sound better, but also kind of following the same wording as the CN version * Update algorithms_are_everywhere.md re-edited the dictionary part from Piyin to just normal Eng dictionary. again thank you very much hpstory for you suggestion. * Update algorithms_are_everywhere.md --------- Co-authored-by: Yudong Jin <krahets@163.com> * Prepare merging into main branch. * Update buttons * Update Dockerfile * Update index.md * Update index.md * Update README * Fix index.md * Fix mkdocs-en.yml --------- Co-authored-by: Yuelin Xin <sc20yx2@leeds.ac.uk> Co-authored-by: Phoenix Xie <phoenixx0415@gmail.com> Co-authored-by: Sizhuo Long <longsizhuo@gmail.com> Co-authored-by: Spark <qizhang94@outlook.com> Co-authored-by: Thomas <thomasqiu7@gmail.com> Co-authored-by: ThomasQiu <thomas.qiu@mnfgroup.limited> Co-authored-by: K3v123 <123932560+K3v123@users.noreply.github.com> Co-authored-by: Jin <36914748+yanedie@users.noreply.github.com>
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Hello Algo
Data Structures and Algorithms Crash Course with Animated Illustrations and Off-the-Shelf Code
Endorsements
Quote
"An easy-to-understand book on data structures and algorithms, which guides readers to learn by minds-on and hands-on. Strongly recommended for algorithm beginners!"
—— Junhui Deng, Professor of Computer Science, Tsinghua University
Quote
"If I had 'Hello Algo' when I was learning data structures and algorithms, it would have been 10 times easier!"
—— Mu Li, Senior Principal Scientist, Amazon
Animated illustrations
Easy to understand
Smooth learning curve
"A picture is worth a thousand words."
Off-the-Shelf Code
Multi programming languages
Run with one click
"Talk is cheap. Show me the code."
Learning Together
Discussion and questions welcome
Readers progress together
"Chase the wind and moon, never stopping"
"Beyond the plains, there are spring mountains"
Preface
Two years ago, I shared the "Sword Offer" series of problem solutions on LeetCode, which received much love and support from many students. During my interactions with readers, the most common question I encountered was "How to get started with algorithms." Gradually, I developed a deep interest in this question.
Blindly solving problems seems to be the most popular method, being simple, direct, and effective. However, problem-solving is like playing a "Minesweeper" game, where students with strong self-learning abilities can successfully clear the mines one by one, but those with insufficient foundations may end up bruised from explosions, retreating step by step in frustration. Thoroughly reading textbooks is also common, but for students aiming for job applications, the energy consumed by graduation, resume submissions, and preparing for written tests and interviews makes tackling thick books a daunting challenge.
If you are facing similar troubles, then you are lucky to have found this book. This book is my answer to this question, not necessarily the best solution, but at least an active attempt. Although this book won't directly land you an Offer, it will guide you through the "knowledge map" of data structures and algorithms, help you understand the shape, size, and distribution of different "mines," and equip you with various "demining methods." With these skills, I believe you can more comfortably solve problems and read literature, gradually building a complete knowledge system.
I deeply agree with Professor Feynman's saying: "Knowledge isn't free. You have to pay attention." In this sense, this book is not entirely "free." To not disappoint the precious "attention" you pay to this book, I will do my utmost, investing the greatest "attention" to complete the creation of this book.
Author
Yudong Jin(Krahets), Senior Algorithm Engineer in a top tech company, Master's degree from Shanghai Jiao Tong University. The highest-read blogger across the entire LeetCode, his published "Illustration of Algorithm Data Structures" has been subscribed to by over 300k.
Contribution
This book is continuously improved with the joint efforts of many contributors from the open-source community. Thanks to each writer who invested their time and energy, listed in the order generated by GitHub:
The code review work for this book was completed by Gonglja, gvenusleo, hpstory, justin‐tse, krahets, night-cruise, nuomi1, Reanon, and sjinzh (listed in alphabetical order). Thanks to them for their time and effort, ensuring the standardization and uniformity of the code in various languages.
Gonglja C, C++ |
gvenusleo Dart |
hpstory C# |
justin-tse JS, TS |
krahets Java, Python |
night-cruise Rust |
nuomi1 Swift |
Reanon Go, C |
sjinzh Rust, Zig |