Prepare for technical interviews at major tech companies by working through 200 carefully sequenced algorithm problems.
Learn algorithm patterns and problem-solving approaches with detailed explanations and diagrams for each topic.
Study data structures like arrays, trees, graphs, and dynamic programming with worked solutions in your preferred language.
Follow a structured learning path that progresses from foundational concepts to advanced algorithm techniques.
This repository is a structured LeetCode study guide written primarily in Chinese that provides a curated learning path through 200 classic algorithm and data structure problems, with detailed explanations, diagrams, and solutions in multiple programming languages. The description and README indicate it contains over 600,000 characters of written content plus over 50 mind maps. The core problem it addresses is that beginning programmers preparing for technical interviews often do not know which problems to solve or in what order, and standard resources do not explain the underlying patterns and thought processes clearly enough. The guide organizes problems by topic in a carefully sequenced order, starting with foundational subjects like arrays and linked lists, then progressing through hash tables, strings, stacks and queues, binary trees, backtracking, greedy algorithms, dynamic programming, and graph theory. Each topic section includes theory explanations before the problems, worked solutions with diagrams and video links, and summary articles. Solutions are provided in C++ as the primary language with additional implementations in Java, Python, Go, and JavaScript contributed by the community. The README is written in Chinese and targets Chinese-speaking developers preparing for technical interviews at major technology companies. It links to a companion website, published book, and video lecture series. You would use this repository if you are a Chinese-speaking developer preparing for software engineering interviews and want a complete, organized system for learning algorithms. The tech stack for the study content itself is Markdown and Shell, with solutions in multiple languages.
Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.