Algorithm Patterns is a study guide and code repository that teaches algorithm problem-solving through reusable templates and structured practice.
The project addresses the challenge of preparing for technical interviews by distilling common algorithmic patterns into reusable templates. Rather than solving problems in arbitrary order, it organizes learning around data structures and algorithmic thinking patterns, including binary trees, linked lists, dynamic programming, sliding windows, and backtracking. Each pattern includes explanation of the thought process, analysis of common mistakes, and practice problems to reinforce understanding. The approach emphasizes topic-based practice over sequential problem-solving, allowing learners to master one pattern type before moving to the next.
This resource suits developers preparing for technical interviews at large companies who want a structured learning path rather than grinding problems randomly. It works best for those starting from scratch or with gaps in algorithmic knowledge, offering both conceptual explanations and Go implementations. The project is language-agnostic in its pattern descriptions but provides code examples in Go. Unlike generic LeetCode problem lists, it explicitly teaches the reasoning behind solutions and groups related problems by technique.
The project maintains a stable, complete collection of materials organized across data structures, basic algorithms, and advanced algorithmic thinking. Documentation is published on Gitbook alongside the repository, providing both online and local access to the content. The codebase includes implementations and examples corresponding to each pattern discussed in the guides.
