3636 < div class ="ambient ambient-left "> </ div >
3737 < div class ="ambient ambient-right "> </ div >
3838 < div class ="grid-overlay " aria-hidden ="true "> </ div >
39+ < div class ="scanline-layer " aria-hidden ="true "> </ div >
3940 < div class ="particle-field " id ="particle-field " aria-hidden ="true "> </ div >
4041
4142 < header class ="site-header " id ="top ">
7071 < section class ="hero section ">
7172 < div class ="hero-copy reveal ">
7273 < p class ="eyebrow "> AIGCmagic Community / AI Career Knowledge System</ p >
73- < h1 > 三年面试五年模拟</ h1 >
74+ < h1 class =" glitch-title " data-text =" 三年面试五年模拟 " > 三年面试五年模拟</ h1 >
7475 < p class ="hero-subtitle "> AIGC算法岗 / 开发岗的面试求职秘籍</ p >
7576 < p class ="hero-description ">
7677 这是一个面向 AI 求职、面试、笔试与成长进阶的系统化内容平台,依托
@@ -79,6 +80,18 @@ <h1>三年面试五年模拟</h1>
7980 研究员与转行学习者。
8081 </ p >
8182
83+ < div class ="hero-terminal " aria-label ="项目能力概览 ">
84+ < div class ="terminal-bar ">
85+ < span > </ span >
86+ < strong > career_os://aigc-interview-book</ strong >
87+ </ div >
88+ < div class ="terminal-lines ">
89+ < span > < b > INPUT</ b > 面试高频考点 / 项目表达 / 前沿趋势</ span >
90+ < span > < b > MODEL</ b > LLM + Agent + 多模态 + 部署工程</ span >
91+ < span > < b > OUTPUT</ b > 系统化知识地图与可复用求职方法</ span >
92+ </ div >
93+ </ div >
94+
8295 < div class ="hero-actions ">
8396 < a class ="button primary " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/blob/main/README.md " target ="_blank " rel ="noreferrer "> 立即阅读</ a >
8497 < a class ="button secondary " href ="https://github.com/WeThinkIn/AIGC-Interview-Book " target ="_blank " rel ="noreferrer "> 查看 GitHub</ a >
@@ -128,7 +141,7 @@ <h1>三年面试五年模拟</h1>
128141 </ div >
129142 < div class ="hero-stats ">
130143 < div class ="stat-card ">
131- < strong > 17+ </ strong >
144+ < strong > 24 </ strong >
132145 < span > 核心内容板块</ span >
133146 </ div >
134147 < div class ="stat-card ">
@@ -228,85 +241,157 @@ <h3>社区价值</h3>
228241 < section class ="section " id ="knowledge ">
229242 < div class ="section-heading reveal ">
230243 < p class ="eyebrow "> Knowledge Map</ p >
231- < h2 > 覆盖 AI 岗位核心能力的知识地图 </ h2 >
244+ < h2 > 同步 GitHub 最新目录的 AI 求职知识地图 </ h2 >
232245 < p >
233- 从基础理论到前沿方向,从算法理解到工程落地,从经典问题到 AIGC
234- 时代新能力,构建一张既有知识广度、又有岗位针对性的内容地图 。
246+ 当前主页按仓库 README 与 GitHub 主分支目录重新整理模块入口,从 AIGC 图像、视频、大模型、多模态、Agent
247+ 到部署、编程、数据结构、计算机基础与开放性问题,形成一张可直接跳转阅读的完整索引 。
235248 </ p >
236249 </ div >
237250
238251 < div class ="knowledge-grid ">
239252 < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/算法岗面试求职宝典 " target ="_blank " rel ="noreferrer ">
240253 < span class ="knowledge-icon "> 01</ span >
241- < h3 > 面试求职宝典 </ h3 >
242- < p > 简历模版、求职攻略、面经整理、薪资与内推信息、高频答疑 。</ p >
254+ < h3 > 算法岗面试求职宝典 </ h3 >
255+ < p > 简历模版、求职攻略、面经整理、招聘内推、公司清单与高频答疑 。</ p >
243256 < span class ="chip-row "> < span > Interview</ span > < span > Career</ span > </ span >
244257 </ a >
245- < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/大模型基础 " target ="_blank " rel ="noreferrer ">
258+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AIGC图像创作%26AI绘画基础 " target ="_blank " rel ="noreferrer ">
246259 < span class ="knowledge-icon "> 02</ span >
247- < h3 > LLM / 大模型 </ h3 >
248- < p > 基础知识、架构、训练微调、RAG、推理应用、安全性与评测 。</ p >
249- < span class ="chip-row "> < span > LLM </ span > < span > RAG </ span > </ span >
260+ < h3 > AIGC 图像创作 & AI 绘画基础 </ h3 >
261+ < p > Stable Diffusion、FLUX、LoRA、ControlNet、可控生成、训练优化与图像生成框架 。</ p >
262+ < span class ="chip-row "> < span > Diffusion </ span > < span > FLUX </ span > </ span >
250263 </ a >
251- < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AI%20Agent基础 " target ="_blank " rel ="noreferrer ">
264+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AI视频基础 " target ="_blank " rel ="noreferrer ">
252265 < span class ="knowledge-icon "> 03</ span >
253- < h3 > AI Agent </ h3 >
254- < p > 智能体工作流、规划模式、框架理解与 Agent 化应用能力 。</ p >
255- < span class ="chip-row "> < span > Agent </ span > < span > Workflow </ span > </ span >
266+ < h3 > AI 视频基础 </ h3 >
267+ < p > 视频生成、视频理解、视频编辑、人体视频、虚拟试衣、训练微调与评测体系 。</ p >
268+ < span class ="chip-row "> < span > Video </ span > < span > Generation </ span > </ span >
256269 </ a >
257- < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AI多模态基础 " target ="_blank " rel ="noreferrer ">
270+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/大模型基础 " target ="_blank " rel ="noreferrer ">
258271 < span class ="knowledge-icon "> 04</ span >
259- < h3 > 多模态 </ h3 >
260- < p > 模态编码器、输入输出映射器、核心模型范式与前沿方向 。</ p >
261- < span class ="chip-row "> < span > Multimodal </ span > < span > VL </ span > </ span >
272+ < h3 > 大模型基础 </ h3 >
273+ < p > 基础知识、经典架构、训练微调、强化学习、RAG、推理应用、安全性与评测 。</ p >
274+ < span class ="chip-row "> < span > LLM </ span > < span > RAG </ span > </ span >
262275 </ a >
263- < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AI视频基础 " target ="_blank " rel ="noreferrer ">
276+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AI多模态基础 " target ="_blank " rel ="noreferrer ">
264277 < span class ="knowledge-icon "> 05</ span >
265- < h3 > AI 视频 </ h3 >
266- < p > 视频生成、编辑、理解、训练优化与统一视频大模型趋势 。</ p >
267- < span class ="chip-row "> < span > Video </ span > < span > Generation </ span > </ span >
278+ < h3 > AI 多模态基础 </ h3 >
279+ < p > 多模态理论、主干模型、核心组件、Qwen-VL、InternVL、GLM-V 与前沿技术 。</ p >
280+ < span class ="chip-row "> < span > Multimodal </ span > < span > VL </ span > </ span >
268281 </ a >
269- < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AI绘画基础 " target ="_blank " rel ="noreferrer ">
282+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AI%20Agent基础 " target ="_blank " rel ="noreferrer ">
270283 < span class ="knowledge-icon "> 06</ span >
271- < h3 > AIGC 图像生成 </ h3 >
272- < p > 扩散模型、LoRA、ControlNet、Flux、Stable Diffusion 与实操 。</ p >
273- < span class ="chip-row "> < span > Diffusion </ span > < span > Image </ span > </ span >
284+ < h3 > AI Agent 基础 </ h3 >
285+ < p > Agent 基础知识、设计模式、经典智能体范式与智能体应用开发能力 。</ p >
286+ < span class ="chip-row "> < span > Agent </ span > < span > Workflow </ span > </ span >
274287 </ a >
275- < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/深度学习基础 " target ="_blank " rel ="noreferrer ">
288+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/数字人基础 " target ="_blank " rel ="noreferrer ">
276289 < span class ="knowledge-icon "> 07</ span >
277- < h3 > 传统深度学习 </ h3 >
278- < p > 核心概念、网络结构、训练优化、注意力机制与工程实践 。</ p >
279- < span class ="chip-row "> < span > DL </ span > < span > Training </ span > </ span >
290+ < h3 > 数字人基础 </ h3 >
291+ < p > 2D/3D 数字人生成、3D 数据表示、渲染基础与数字人应用链路 。</ p >
292+ < span class ="chip-row "> < span > Digital Human </ span > < span > 3D </ span > </ span >
280293 </ a >
281- < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/经典模型 " target ="_blank " rel ="noreferrer ">
294+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/深度学习基础 " target ="_blank " rel ="noreferrer ">
282295 < span class ="knowledge-icon "> 08</ span >
283- < h3 > CV / NLP / RL 经典模型 </ h3 >
284- < p > 目标检测、分割、OCR、ReID、跟踪、自然语言处理与强化学习 。</ p >
285- < span class ="chip-row "> < span > CV </ span > < span > NLP </ span > < span > RL </ span > </ span >
296+ < h3 > 深度学习基础 </ h3 >
297+ < p > 数学基础、核心概念、网络层、激活函数、深度网络架构、训练优化与框架工具 。</ p >
298+ < span class ="chip-row "> < span > DL </ span > < span > Training </ span > </ span >
286299 </ a >
287300 < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/机器学习基础 " target ="_blank " rel ="noreferrer ">
288301 < span class ="knowledge-icon "> 09</ span >
289- < h3 > 机器学习 </ h3 >
290- < p > 经典算法、损失函数、模型评估与优化方法,高频基础完整覆盖 。</ p >
302+ < h3 > 机器学习基础 </ h3 >
303+ < p > 机器学习基础概念、 经典算法、损失函数、模型评估与优化方法。</ p >
291304 < span class ="chip-row "> < span > ML</ span > < span > Eval</ span > </ span >
292305 </ a >
293306 < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/模型部署基础 " target ="_blank " rel ="noreferrer ">
294307 < span class ="knowledge-icon "> 10</ span >
295- < h3 > 模型部署 </ h3 >
296- < p > vLLM、SGLang、调度优化、服务化部署与工业级推理系统认知 。</ p >
308+ < h3 > 模型部署基础 </ h3 >
309+ < p > 模型部署概念、推理框架、数据处理、模型压缩、自定义算子、边云端与大模型部署 。</ p >
297310 < span class ="chip-row "> < span > Deploy</ span > < span > Serving</ span > </ span >
298311 </ a >
299- < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/编程基础:Python " target ="_blank " rel ="noreferrer ">
312+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/经典模型 " target ="_blank " rel ="noreferrer ">
300313 < span class ="knowledge-icon "> 11</ span >
301- < h3 > 开发基础 </ h3 >
302- < p > Python、C/C++、数据结构、算法题、计算机基础,夯实工程底座 。</ p >
303- < span class ="chip-row "> < span > Python </ span > < span > C++ </ span > < span > CS </ span > </ span >
314+ < h3 > 经典模型 </ h3 >
315+ < p > 目标检测、图像分类、图像分割、OCR、ReID、人脸、跟踪、NLP 与强化学习高频考点 。</ p >
316+ < span class ="chip-row "> < span > CV </ span > < span > NLP </ span > < span > RL </ span > </ span >
304317 </ a >
305- < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/具身智能基础 " target ="_blank " rel ="noreferrer ">
318+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/编程基础:Python " target ="_blank " rel ="noreferrer ">
306319 < span class ="knowledge-icon "> 12</ span >
307- < h3 > 前沿方向</ h3 >
308- < p > 自动驾驶、具身智能、元宇宙、AGI 等方向的岗位关联能力外延。</ p >
309- < span class ="chip-row "> < span > Embodied AI</ span > < span > AGI</ span > </ span >
320+ < h3 > 编程基础:Python</ h3 >
321+ < p > Python 基础、进阶知识与 AI 行业常用 Python 代码案例高频考点。</ p >
322+ < span class ="chip-row "> < span > Python</ span > < span > Code</ span > </ span >
323+ </ a >
324+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/编程基础:C和C%2B%2B " target ="_blank " rel ="noreferrer ">
325+ < span class ="knowledge-icon "> 13</ span >
326+ < h3 > 编程基础:C 和 C++</ h3 >
327+ < p > C/C++ 基础、进阶知识、实战经典考点与算法工程师常见手撕场景。</ p >
328+ < span class ="chip-row "> < span > C++</ span > < span > System</ span > </ span >
329+ </ a >
330+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/大厂高频算法题 " target ="_blank " rel ="noreferrer ">
331+ < span class ="knowledge-icon "> 14</ span >
332+ < h3 > 大厂高频算法题</ h3 >
333+ < p > LeetCode Hot100、面试笔试算法题精华、大厂高频手撕算法题与公司真题。</ p >
334+ < span class ="chip-row "> < span > LeetCode</ span > < span > Algorithm</ span > </ span >
335+ </ a >
336+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/数据结构基础 " target ="_blank " rel ="noreferrer ">
337+ < span class ="knowledge-icon "> 15</ span >
338+ < h3 > 数据结构基础</ h3 >
339+ < p > 面试中常见的数据结构知识、复杂度表达与算法题底层能力补齐。</ p >
340+ < span class ="chip-row "> < span > DSA</ span > < span > Complexity</ span > </ span >
341+ </ a >
342+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/计算机基础 " target ="_blank " rel ="noreferrer ">
343+ < span class ="knowledge-icon "> 16</ span >
344+ < h3 > 计算机基础</ h3 >
345+ < p > 操作系统、计算机网络、数据库与计算机工程基础高频知识点。</ p >
346+ < span class ="chip-row "> < span > OS</ span > < span > Network</ span > < span > DB</ span > </ span >
347+ </ a >
348+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/开放性问题 " target ="_blank " rel ="noreferrer ">
349+ < span class ="knowledge-icon "> 17</ span >
350+ < h3 > 开放性问题</ h3 >
351+ < p > AI 行业趋势、业务场景判断、算法工程师个人成长与面试表达题。</ p >
352+ < span class ="chip-row "> < span > Open-ended</ span > < span > Career</ span > </ span >
353+ </ a >
354+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/具身智能基础 " target ="_blank " rel ="noreferrer ">
355+ < span class ="knowledge-icon "> 18</ span >
356+ < h3 > 具身智能基础</ h3 >
357+ < p > 具身智能基础知识与视觉、语言、动作结合下的新一代智能体能力外延。</ p >
358+ < span class ="chip-row "> < span > Embodied AI</ span > < span > VLA</ span > </ span >
359+ </ a >
360+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/数学基础 " target ="_blank " rel ="noreferrer ">
361+ < span class ="knowledge-icon "> 19</ span >
362+ < h3 > 数学基础</ h3 >
363+ < p > 支撑机器学习、深度学习与大模型理解的核心数学基础入口。</ p >
364+ < span class ="chip-row "> < span > Math</ span > < span > Foundation</ span > </ span >
365+ </ a >
366+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/深度学习基础(精华版) " target ="_blank " rel ="noreferrer ">
367+ < span class ="knowledge-icon "> 20</ span >
368+ < h3 > 深度学习基础(精华版)</ h3 >
369+ < p > 机器学习、深度学习基础、网络架构、训练优化与框架工具的高频精华版。</ p >
370+ < span class ="chip-row "> < span > DL</ span > < span > Essentials</ span > </ span >
371+ </ a >
372+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/大模型基础(精华版) " target ="_blank " rel ="noreferrer ">
373+ < span class ="knowledge-icon "> 21</ span >
374+ < h3 > 大模型基础(精华版)</ h3 >
375+ < p > 面向大模型方向冲刺复习的精华版入口,用于快速建立 LLM 高频考点框架。</ p >
376+ < span class ="chip-row "> < span > LLM</ span > < span > Essentials</ span > </ span >
377+ </ a >
378+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AI多模态基础(精华版) " target ="_blank " rel ="noreferrer ">
379+ < span class ="knowledge-icon "> 22</ span >
380+ < h3 > AI 多模态基础(精华版)</ h3 >
381+ < p > 多模态理论、核心组件、基石模型、主流模型、原生模型与下游应用高频考点。</ p >
382+ < span class ="chip-row "> < span > Multimodal</ span > < span > Essentials</ span > </ span >
383+ </ a >
384+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/AI视频基础(精华版) " target ="_blank " rel ="noreferrer ">
385+ < span class ="knowledge-icon "> 23</ span >
386+ < h3 > AI 视频基础(精华版)</ h3 >
387+ < p > AI 视频核心知识、经典架构、可控视频生成、视频理解、视频编辑与性能优化。</ p >
388+ < span class ="chip-row "> < span > Video</ span > < span > Essentials</ span > </ span >
389+ </ a >
390+ < a class ="knowledge-card reveal " href ="https://github.com/WeThinkIn/AIGC-Interview-Book/tree/main/模型部署基础(精华版) " target ="_blank " rel ="noreferrer ">
391+ < span class ="knowledge-icon "> 24</ span >
392+ < h3 > 模型部署基础(精华版)</ h3 >
393+ < p > 推理部署综述、ONNX、TensorRT、大模型部署技术、性能分析与调优工具。</ p >
394+ < span class ="chip-row "> < span > Deploy</ span > < span > Essentials</ span > </ span >
310395 </ a >
311396 </ div >
312397 </ section >
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