AIGC Interview Book

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Basic Information

This repository is a curated, community-maintained handbook for preparing for AIGC algorithm and development job interviews. It collects interview questions, exam and hiring experience, preparation strategies, resume templates, job hunting advice, company-specific high-frequency algorithm problems and study guides spanning AIGC, deep learning, machine learning, computer vision, natural language processing, reinforcement learning, autonomous driving, multimodal models, model deployment and engineering topics. The content is organized into topical chapters and linked subfolders for areas such as AI painting, AI video, large models, multimodal systems, model deployment and programming basics. It is authored and edited by experienced practitioners and community contributors and is positioned as both a practical interview prep resource and a reference for teaching, research and on-the-job learning in AIGC-related roles.

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Features
The README and table of contents show modular coverage of core areas, including algorithm interview guides, coding foundations in Python and C/C++, data structures, classic models, model deployment, high-frequency company algorithm problems, and open questions. It includes resume templates, interview techniques, hiring timeline and salary notes, curated problem sets and study roadmaps. The project links to community resources and paid study groups for deeper support, lists editors and contributors with industry and competition experience, and invites PRs and issues for contributions. The repo is maintained with ongoing updates and is intended to aggregate both beginner and intermediate level materials and practical exam items.
Use Cases
The repository helps job seekers plan and prioritize interview preparation by providing a structured syllabus across AIGC and engineering topics, targeted high-frequency problems from large companies, and practical advice on resumes, interviewing and company pipelines. It supplies curated learning paths for algorithm and development roles, reference material for instructors and researchers, and community channels for Q&A and peer support. Contributors can submit real interview experiences and updates, enabling the content to reflect current industry practice. The collection reduces search time for candidates, offers focused practice material for technical interviews, and provides recruiters or interviewers with a repository of vetted questions and topic outlines.

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