by WeThinkIn · Codex Skill · ★ 3.6k
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🔥【三年面试五年模拟】AIGC算法岗/开发岗的面试求职秘籍 【Three Years of Interviews, Five Years of Practice】The Ultimate Guide to AIGC Interview、LLMs Interview、AI Agent Interview、Deep Learning Interview、Algorithm Engineer Interview 免费H800算力,立即领取! 🏆AIGC算法岗方向: 涵盖AIGC、传统深度学习、自动驾驶、AI Agent、机器学习、计算机视觉、自然语言处理、强化学习、大数据挖掘、具身智能、元宇宙、AGI等。 🏆AIGC开发岗方向: 涵盖Python、Java、C/C++、Go、嵌入式、前端、后端、测试、运维等。 🚀本项目凝聚了AIGC时代众多一线AIGC算法专家的行业经验与深度洞察,涵盖AIGC完整知识架构、AIGC大厂内推、AIGC面试经验、AIGC公司指南/辛秘、AI校招时间表、AIGC面试准备、AIGC薪资爆料、AIGC刷题指南、AIGC求职答疑等干货资源。本项目的核心内容均取材于编者们在AI行业中的工作、研究、竞赛经验,以及对各互联网大厂/AIGC明星公司的AIGC岗位笔试/面试题提炼。 💡本项目也可作为高等学府AIGC相关专业的研究、教学、竞赛以及学习的参考用书;还可为AIGC、传统深度学习以及自动驾驶领域的初、中级技术人员提供思路参考,尤其适合AIGC求职者和提供相关AICG算法岗位的面试官阅读研究。 👍本项目的持续构建/维护十分不易,希望大家能多多star~。Star本项目,你就获得了0.5个心仪的offer;再分享本项目,你就获得了0.75个心仪offer!在这里,Rocky祝大家求职顺利、工作顺利! 📖目录 :star: 算法岗面试求职宝典(包含简历模版、求职攻略、面试经验、面试技巧等通用AIGC面试技巧) :art: AI绘画基础 :clapper: AI视频基础 :sparkler: 大模型基础 :trident: AI多模态基础 :videogame: AI Agent基础 :shipit: 数字人基础 :closedbook: 深度学习基础 :bluebook: 机器学习基础 :europeancastle: 模型部署基础 :stars: 经典模型 :snake: 编程基础:Python :barchart: 编程基础:C和C++ :boom: 大厂高频算法题 :battery: 数据结构基础 :computer: 计算机基础 :chartwithupwardstrend: 开放性问题 [2025年AI算法岗求职群&学习交流社区](https://mp.weixin.qq.com/s/ssEJjHBoJ
| Stars | 3,626 |
| Forks | 394 |
| Category | Codex Skill |
| License | GPL-3.0 |
| Quality Score | 32.84/100 |
| Last Updated | 2026-05-05 |
| Created | 2023-10-14 |
| Est. Tokens | ~27036k |
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AIGC-Interview-Book is 【三年面试五年模拟】AIGC/LLM/AI Agent算法工程师面试秘籍。涵盖AIGC、LLM大模型、AI Agent、传统深度学习、自动驾驶、机器学习、计算机视觉、自然语言处理、强化学习、大数据挖掘、具身智能、元宇宙、AGI等AI行业面试笔试干货经验与核心知识。. It is categorized as a Codex Skill with 3.6k GitHub stars.
You can find installation instructions and usage details in the AIGC-Interview-Book GitHub repository at github.com/WeThinkIn/AIGC-Interview-Book. The project has 3.6k stars and 394 forks, indicating an active community.
AIGC-Interview-Book is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to AIGC-Interview-Book on Agent Skills Hub include ai-engineering-from-scratch, prompttools, LLMCompiler. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.