krahets/leetcode-book

《剑指 Offer》《图解算法数据结构》《Krahets 笔面试精选 88 题》Python, Java, C++ 解题代码

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Summary Information

Updated 2 minutes ago
Added to GitGenius on September 7th, 2026
Created on December 17th, 2021
Open Issues & Pull Requests: 6 (+0)
GitHub issues: Enabled
Number of forks: 901
Total Stargazers: 8,492 (+0)
Total Subscribers: 49 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 15.7 hours
Mean response time: 41.1 hours
90th percentile: 2.8 days
Tracked items: 2

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Open issues: 4
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 457 days
Stale 30+ days: 4
Stale 90+ days: 4

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Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

LeetCode-Book is a curated collection of algorithm problem solutions and explanations designed for interview preparation and algorithm learning.

The project addresses the challenge of preparing for technical interviews by providing illustrated explanations and multi-language implementations of algorithm problems. It organizes solutions across three main collections: problems from the "Sword for Offer" interview classic, a curated set of interview problems, and problems from an illustrated algorithm and data structures guide. Each problem includes detailed explanations paired with clean implementations in Python, Java, and C++.

The tool suits developers preparing for technical interviews at major technology companies, particularly those new to algorithms who benefit from visual explanations alongside code. The project recommends starting with foundational algorithm knowledge before working through these problems systematically, suggesting a pace of two to three problems daily. It emphasizes understanding solutions deeply rather than accumulating quantity, and recommends reviewing problems multiple times to achieve mastery. The repository positions itself as a second step after learning fundamental concepts, with problems selected based on their frequency in actual hiring processes.

The project maintains active engagement with its learning community through structured problem collections and detailed solution documentation. Development focuses on providing comprehensive coverage of core algorithm topics through illustrated explanations paired with working code implementations across multiple programming languages.