[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-BoltzmannEntropy--interviews.ai":3,"tool-BoltzmannEntropy--interviews.ai":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",145895,2,"2026-04-08T11:32:59",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108111,"2026-04-08T11:23:26",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":10,"last_commit_at":59,"category_tags":60,"status":17},4487,"LLMs-from-scratch","rasbt\u002FLLMs-from-scratch","LLMs-from-scratch 是一个基于 PyTorch 的开源教育项目，旨在引导用户从零开始一步步构建一个类似 ChatGPT 的大型语言模型（LLM）。它不仅是同名技术著作的官方代码库，更提供了一套完整的实践方案，涵盖模型开发、预训练及微调的全过程。\n\n该项目主要解决了大模型领域“黑盒化”的学习痛点。许多开发者虽能调用现成模型，却难以深入理解其内部架构与训练机制。通过亲手编写每一行核心代码，用户能够透彻掌握 Transformer 架构、注意力机制等关键原理，从而真正理解大模型是如何“思考”的。此外，项目还包含了加载大型预训练权重进行微调的代码，帮助用户将理论知识延伸至实际应用。\n\nLLMs-from-scratch 特别适合希望深入底层原理的 AI 开发者、研究人员以及计算机专业的学生。对于不满足于仅使用 API，而是渴望探究模型构建细节的技术人员而言，这是极佳的学习资源。其独特的技术亮点在于“循序渐进”的教学设计：将复杂的系统工程拆解为清晰的步骤，配合详细的图表与示例，让构建一个虽小但功能完备的大模型变得触手可及。无论你是想夯实理论基础，还是为未来研发更大规模的模型做准备",90106,"2026-04-06T11:19:32",[35,15,13,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":76,"owner_email":76,"owner_twitter":76,"owner_website":77,"owner_url":78,"languages":76,"stars":79,"forks":80,"last_commit_at":81,"license":76,"difficulty_score":82,"env_os":83,"env_gpu":84,"env_ram":84,"env_deps":85,"category_tags":88,"github_topics":90,"view_count":32,"oss_zip_url":76,"oss_zip_packed_at":76,"status":17,"created_at":110,"updated_at":111,"faqs":112,"releases":123},5637,"BoltzmannEntropy\u002Finterviews.ai","interviews.ai","It is my belief that you, the postgraduate students and job-seekers for whom the book is primarily meant will benefit from reading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.","interviews.ai 是一本专为人工智能领域求职者和研究生打造的深度学习面试指南。它收录了数百道涵盖信息论、贝叶斯统计、算法微分、逻辑回归及卷积神经网络等核心话题的面试真题，并提供详尽的解答过程。\n\n在竞争日益激烈的数据科学行业，许多具备扎实背景的候选人往往因缺乏针对性的面试准备而错失良机。interviews.ai 旨在解决这一痛点，帮助用户跨越从“掌握知识”到“通过面试”之间的鸿沟。它不仅提供了系统的复习大纲，更通过具有挑战性的问题和引人入胜的案例故事，帮助用户深化理解，提升解决实际问题的能力。\n\n这本书特别适合拥有量化背景的数据科学准从业者、正在攻读硕士或博士学位的研究生，以及希望巩固基础的研究人员。无论是为了应对高难度的技术面试，还是准备学术考试，它都能提供条理清晰的知识梳理。其独特之处在于将枯燥的理论融入生动的叙事中，让学习过程不再单调，同时确保内容深度足以磨练专业技能。对于渴望进入顶尖 AI 团队的你来说，这是一份不可多得的备考资源。","\n\u003Ch1 align=\"center\">Deep Learning Interviews book: Hundreds of fully solved job interview questions from a wide range of key topics in AI https:\u002F\u002Finterviews.ai\u003C\u002Fh1>\n      \n\u003Cp align=\"center\">\n \u003Ca href=\"#download\">Download PDF\u003C\u002Fa> •\n  \u003Ca href=\"#about\">About\u003C\u002Fa> •    \n  \u003Ca href=\"#about\">Errata\u003C\u002Fa> •    \n\u003C\u002Fp>\n\n\u003Ch1 align=\"center\">    \n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FBoltzmannEntropy_interviews.ai_readme_d91ae3eb1968.png\" width=\"100%\">\u003C\u002Fa>  \n\u003C\u002Fh1>\n\n# A PERSONAL NOTE: \n\"Keep learning, or risk becoming irrelevant.\"\n\nIn this first volume, I purposely present a coherent, cumulative, and content-specific core curriculum of the data science field, including topics\nsuch as information theory, Bayesian statistics, algorithmic differentiation, logistic regression, perceptrons, and convolutional neural networks.\nI hope you will find this book stimulating. \n\nIt is my belief that you the postgraduate students and job-seekers for whom the book is primarily meant will benefit from\nreading it; however, it is my hope that even the most experienced researchers will find it fascinating as well.\n\n## **I would like to solicit corrections, criticisms, and suggestions from students and other readers. Although I have tried to eliminate errors over the multi year process of writing and revising this text, a few undoubtedly remain. In particular, some typographical infelicities will no doubt find their way into the final version.** **I hope you will forgive them.**\n\n**Contact Amir:**\n\n* https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Famirivry\u002F\n\n* https:\u002F\u002Fscholar.google.com.mx\u002Fcitations?user=rQCVwksAAAAJ&hl=iw\n\n\n**Contact Shlomo:**\n\n* https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fquantscientist\u002F\n\n* https:\u002F\u002Fscholar.google.com.mx\u002Fcitations?user=bM0LGgcAAAAJ&hl\n\n\nThis book is available for purchase through Amazon and other standard distribution channels. Please see the publisher's web page to order the book or to obtain further details on its publication. A manuscript of the book can be found below—it has been made available for personal use only and must not be sold.\n\n* https:\u002F\u002Famazon.com\u002Fauthor\u002Fquantscientist\n\n\n\n---\n# Download \n\n### The PDF is available here: \nhttps:\u002F\u002Farxiv.org\u002Fabs\u002F2201.00650\n\n## Citation\n```\n@misc{kashani2021deep,\n      title={Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI}, \n      author={Shlomo Kashani and Amir Ivry},\n      year={2021},\n      eprint={2201.00650},\n      note = {ISBN 13: 978-1-9162435-4-5 }, \n      url = {https:\u002F\u002Fwww.interviews.ai}, \n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n```\n\n**SELLING OR COMMERCIAL USE IS STRICTLY PROHIBITED**.\nThe user rights of this e-resource are specified in a licence agreement below. \nYou may only use this e-resource for the purposes *private study*. \nAny selling\u002Freselling of its content is strictly prohibited. \n\nThis book (www.interviews.ai) was written for you: an aspiring data scientist with a quantitative background, facing down the gauntlet of the interview process in an increasingly competitive field. For most of you, the interview process is the most significant hurdle between you and a dream job. \nEven though you have the ability, the background, and the motivation to excel in your target position, you might need some guidance on how to get your foot in the door.\n\n\n## About\n\nThe second edition of Deep Learning Interviews (The Amazon Softcover is printed in B&W) is home to hundreds of fully-solved problems, from a wide range of key topics in AI. It is designed to both rehearse interview or exam specific topics and provide machine learning M.Sc.\u002FPh.D. students, and those awaiting an interview a well-organized overview of the field. The problems it poses are tough enough to cut your teeth on and to dramatically improve your skills-but they’re framed within thought-provoking questions and engaging stories.\n\nThat is what makes the volume so specifically valuable to students and job seekers: it provides them with the ability to speak confidently and quickly on any relevant topic, to answer technical questions clearly and correctly, and to fully understand the purpose and meaning of interview questions and answers. Those are powerful, indispensable advantages to have when walking into the interview room.\n\nThe book’s contents is a large inventory of numerous topics relevant to DL job interviews and graduate level exams. That places this work at the forefront of the growing trend in science to teach a core set of practical mathematical and computational skills. It is widely accepted that the training of every computer scientist must include the fundamental theorems of ML, and AI appears in the curriculum of nearly every university. This volume is designed as an excellent reference for graduates of such programs.\n\n- The book spans almost 400 pages\n- Hundreds of fully-solved problems\n- Problems from numerous areas of deep learning\n- Clear diagrams and illustrations\n- A comprehensive index\n- Step-by-step solutions to problems\n- Not just the answers given, but the work shown\n- Not just the work shown, but reasoning given where appropriate\n\nThis book was written for you: an aspiring data scientist with a quantitative background, facing down the gauntlet of the interview process in an increasingly competitive field. For most of you, the interview process is the most significant hurdle between you and a dream job. Even though you have the ability, the background, and the motivation to excel in your target position, you might need some guidance on how to get your foot in the door.\nYour curiosity will pull you through the book’s problem sets, formulas, and instructions, and as you progress, you’ll deepen your understanding of deep learning. There are intricate connections between calculus, logistic regression, entropy, and deep learning theory; work through the book, and those connections will feel intuitive.\n\n## CORE SUBJECT AREAS (VOLUME-I):\n\nVOLUME-I of the book focuses on statistical perspectives and blends background fundamentals with core ideas and practical knowledge. There are dedicated chapters on:\n\n- Information Theory\n- Calculus & Algorithmic Differentiation\n- Bayesian Deep Learning & Probabilistic Programming\n- Logistic Regression\n- Ensemble Learning\n- Feature Extraction\n- Deep Learning: expanded chapter (100+ pages)\n\nThese chapters appear alongside numerous in-depth treatments of topics in Deep Learning with code examples in PyTorch, Python and C++.\n \n \n## Disclaimers\n \n - \"PyTorch\" is a trademark of Facebook.\n\n## Licensing\n\n- Copyright © [Shlomo Kashani, author of the book \"Deep Learning Interviews\"](www.interviews.ai)\nShlomo Kashani, Author of the book _Deep Learning Interviews_ www.interviews.ai: entropy@interviews.ai\n\n\u003Ch1 align=\"center\">    \n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FBoltzmannEntropy_interviews.ai_readme_20560fa7c4c3.png\" width=\"50%\">\u003C\u002Fa>  \n\u003C\u002Fh1>\n\n# Errata (May not be up to date)\n## ***Minor corrections are not included.*** \n\nThank you to all the readers who pointed out these issues. \n**Errata for the version** **03\u002F12\u002F2020** **printing and reflected in the online version:**\n\n1.  Question number **PRB-267 -CH.PRB- 8.91** was removed due to lack of clarity \n2.  Question number **PRB-115 - CH.PRB- 5.16** was removed due to lack of clarity \n\n**Errata for the version** **05\u002F12\u002F2020** **printing and reflected in the online version:**\n\n1. Page 230,  Question number **PRB-178** amend “startified scross validation“ TO “stratified cross validation.“\n2. Page 231,  Question number **PRB-181** added a ” .“  after data-folds\n3. Page 231,  Question number **PRB-191** amend “an” to “a”\n4. Page 234,  Question number **PRB-192** “in” repeated twice \n5. Page 236,  Question number **PRB-194 amend** “approached” to “approaches“, “arr” to “arr001”\n6. Page 247,  Question number **PRB-210 amend** “an” to “a”\n7. Page 258,  Question number **PRB-227 amend**  “A confusion metrics” to “A confusion matrix” \n8. Page 271,  Question number **PRB-240 amend**  “MaxPool2D(4,4,)” to “MaxPool2D(4,4)”\n9. Page 273,  Question number **PRB-243 amend**  “identity” to “identify”\n10. Page 281,  Question number **PRB-254 amend**  “suggest” to “suggests”\n11. Page 283,  Question number **PRB-256**  “happening” misspelled \n12. Page 286,  “L1, L2” amended to “Norms” \n13. Page 288,  Question number **SOL-184**  **amend** “the full” to “is the full”\n14. Page 298,  Question number **SOL-208**  **amend** “ou1” to “out”\n15. Page 319,  Question number **SOL-240**  **amend** “torch.Size([1, 32, 222, 222]).”  to “torch.size([1, 32, 222, 222]).“\n16. Page 283,  Question number **PRB-256**  “happening” was misspelled \n\n**Errata for the version** **07\u002F12\u002F2020** **printing and reflected in the online version:**\n\n1. Page 187,  Question number **PRB-140** two missing plots (6.3, 6.4) which did not render correctly on the print version \n![ball001.png](https:\u002F\u002Fimages.squarespace-cdn.com\u002Fcontent\u002Fv1\u002F5c33c435f93fd4233f157b43\u002F1607530038262-PGU5F0YDMFA9NON3NKZA\u002Fball001.png?format=500w)\n\n\n6.3\n\n![ball002.png](https:\u002F\u002Fimages.squarespace-cdn.com\u002Fcontent\u002Fv1\u002F5c33c435f93fd4233f157b43\u002F1607530124438-1U0OIE7QPO0DSMP8LKBS\u002Fball002.png?format=500w)\n\n\n6.4\n**Errata for the version** **09\u002F21\u002F2020** **printing and reflected in the online version:**\n\n1. Page 34,  Solution number **SOL-19** , 0.21886 should be 0.21305 and 0.21886 ± 1.95 × 0.21886  should be **0.21305** ± 1.95 × 0.21886 \n2. Page 36-7,  Solution number **SOL-21** ,4.8792\u002F0.0258 = **189.116** and not 57.3  and pi(33) = 0.01748 and not pi(33) = **0.211868**.\n3. Page 49, **PRB-47**  “What is the probability that the expert is a **monkey**“ should be “What is the probability that the expert is a **human**” \n\n**Errata for the version** **09\u002F22\u002F2020** **printing and reflected in the online version:**\n\n1. Page 73,  Solution number **SOL-56**  should read ”The Hessian is generated by **differentiating**” \n2. Page 57,  Problem number **PRB-65**  should read ”**two** neurons” \n\n**Errata for the version** **09\u002F24\u002F2020** **printing and reflected in the online version:**\n\n1. Page 78,  Solution number **SOL-64** , the OnOffLayer is off only if at least 150 out of 200 neurons are off. Therefore, this may be represented as a Binomial distribution  and the probability for the layer to be off is :\n![2020-12-24 21_08_52-E__Sync_branded_interviews.ai_amazon_21-12-2020_chap_bayes.tex - TeXstudio.png](https:\u002F\u002Fimages.squarespace-cdn.com\u002Fcontent\u002Fv1\u002F5c33c435f93fd4233f157b43\u002F1608836963860-U8ZD3L5L4IL5QKOAUZD8\u002F2020-12-24+21_08_52-E__Sync_branded_interviews.ai_amazon_21-12-2020_chap_bayes.tex+-+TeXstudio.png?format=750w)\n\n\n\n\n","\u003Ch1 align=\"center\">深度学习面试题集：涵盖人工智能多个关键领域的数百道完整解答面试题 https:\u002F\u002Finterviews.ai\u003C\u002Fh1>\n      \n\u003Cp align=\"center\">\n \u003Ca href=\"#download\">下载PDF\u003C\u002Fa> •\n  \u003Ca href=\"#about\">简介\u003C\u002Fa> •    \n  \u003Ca href=\"#about\">勘误\u003C\u002Fa> •    \n\u003C\u002Fp>\n\n\u003Ch1 align=\"center\">    \n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FBoltzmannEntropy_interviews.ai_readme_d91ae3eb1968.png\" width=\"100%\">\u003C\u002Fa>  \n\u003C\u002Fh1>\n\n# 个人寄语：\n“持续学习，否则就有被淘汰的风险。”\n\n在本卷中，我特意呈现了一套连贯、循序渐进且内容聚焦的数据科学核心课程，涵盖信息论、贝叶斯统计、算法微分、逻辑回归、感知机以及卷积神经网络等主题。希望本书能激发您的思考。\n\n我相信，本书主要面向的研究生和求职者会从中受益；同时，我也期待即便是经验丰富的研究人员能够从中获得启发。\n\n## **我诚挚地邀请各位学生及其他读者提出指正、批评与建议。尽管我在多年写作与修订过程中已尽力避免错误，但仍难免存在疏漏。尤其是部分排版上的小瑕疵，很可能出现在最终版本中。** **敬请谅解。**\n\n**联系Amir：**\n\n* https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Famirivry\u002F\n\n* https:\u002F\u002Fscholar.google.com.mx\u002Fcitations?user=rQCVwksAAAAJ&hl=iw\n\n\n**联系Shlomo：**\n\n* https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fquantscientist\u002F\n\n* https:\u002F\u002Fscholar.google.com.mx\u002Fcitations?user=bM0LGgcAAAAJ&hl\n\n\n本书可通过亚马逊及其他常规渠道购买。请访问出版社官网订购或获取更多出版详情。下方提供了本书的手稿——仅供个人使用，严禁出售。\n\n* https:\u002F\u002Famazon.com\u002Fauthor\u002Fquantscientist\n\n\n\n---\n# 下载 \n\n### PDF在此处提供： \nhttps:\u002F\u002Farxiv.org\u002Fabs\u002F2201.00650\n\n## 引用\n```\n@misc{kashani2021deep,\n      title={深度学习面试题集：涵盖人工智能多个关键领域的数百道完整解答面试题}, \n      author={Shlomo Kashani 和 Amir Ivry},\n      year={2021},\n      eprint={2201.00650},\n      note = {ISBN 13: 978-1-9162435-4-5 }, \n      url = {https:\u002F\u002Fwww.interviews.ai}, \n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n```\n\n**严禁销售或商业用途**。本电子资源的使用权利由下文中的许可协议规定。您仅可将此资源用于*私人学习*目的。任何对其内容的销售或转售均被严格禁止。\n\n本书（www.interviews.ai）专为具有量化背景、即将步入日益竞争激烈的就业市场的有志数据科学家而作。对大多数人而言，面试环节是通往理想工作的最大障碍。即便您具备胜任目标岗位的能力、背景与动力，也可能需要一些指导来帮助您顺利进入职场。\n\n## 关于\n\n《深度学习面试题集》第二版（亚马逊平装本为黑白印刷）收录了来自人工智能各关键领域的数百道完整解答题目。它既可用于模拟面试或考试场景，也能为机器学习硕士\u002F博士生以及正在准备面试的人士提供一份条理清晰的领域概览。书中所涉及的问题难度适中，足以锻炼您的实战能力并显著提升技能水平，同时以引人深思的问题和生动的故事为背景。\n\n这正是本书对在校生和求职者尤为珍贵之处：它使读者能够自信而迅速地讨论相关话题，清晰准确地回答技术问题，并深入理解面试问答的目的与意义。这些优势在面试现场显得尤为重要。\n\n本书内容囊括了大量与深度学习职位面试及研究生入学考试相关的主题，使其处于当前科学界强调教授实用数学与计算技能这一趋势的前沿。如今，普遍认为每位计算机科学专业学生的培养都应包含机器学习的基本定理，而人工智能也几乎已成为所有高校课程的一部分。因此，本书可作为此类项目毕业生的优秀参考书。\n\n- 全书近400页\n- 数百道完整解答题目\n- 涵盖深度学习多个领域\n- 清晰的图表与插图\n- 完整的索引\n- 题目解答步骤详尽\n- 不仅给出答案，还展示解题过程\n- 在适当情况下，还会说明解题思路\n\n本书专为具有量化背景、即将面对日益激烈就业市场竞争的有志数据科学家而写。对大多数读者而言，面试环节是通往梦想工作的最大障碍。即使您具备胜任目标岗位所需的能力、背景和动力，可能仍需一些指导来帮助您成功迈入职业生涯。\n\n您的好奇心将驱使您一步步探索书中的习题、公式和说明；随着阅读的深入，您对深度学习的理解也将不断加深。微积分、逻辑回归、熵理论与深度学习原理之间存在着错综复杂的联系。通过研读本书，您将逐渐体会到这些概念之间的内在关联。\n\n## 核心主题领域（第一卷）：\n\n本书第一卷侧重于统计学视角，将基础知识与核心理念及实践性知识相结合。其中设有专门章节探讨：\n\n- 信息论\n- 微积分与算法微分\n- 贝叶斯深度学习与概率编程\n- 逻辑回归\n- 集成学习\n- 特征提取\n- 深度学习：扩展章节（100余页）\n\n这些章节还配有大量关于深度学习主题的深入解析，并附有PyTorch、Python和C++代码示例。\n \n \n## 免责声明\n \n - “PyTorch”是Facebook的注册商标。\n\n## 许可协议\n\n- 版权所有 © [Shlomo Kashani，《深度学习面试题集》作者](www.interviews.ai)\nShlomo Kashani，《深度学习面试题集》作者 www.interviews.ai：entropy@interviews.ai\n\n\u003Ch1 align=\"center\">    \n  \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FBoltzmannEntropy_interviews.ai_readme_20560fa7c4c3.png\" width=\"50%\">\u003C\u002Fa>  \n\u003C\u002Fh1>\n\n# 勘误（可能未更新）\n\n## ***未包含细微更正。*** \n\n感谢所有指出这些问题的读者。 \n**2020年3月12日印刷版及在线版本中的勘误：**\n\n1. 由于表述不清，删除了第**PRB-267 -CH.PRB- 8.91**题。\n2. 由于表述不清，删除了第**PRB-115 - CH.PRB- 5.16**题。\n\n**2020年5月12日印刷版及在线版本中的勘误：**\n\n1. 第230页，第**PRB-178**题将“startified scross validation”修改为“stratified cross validation”。\n2. 第231页，第**PRB-181**题在data-folds后添加了一个“.”。\n3. 第231页，第**PRB-191**题将“an”改为“a”。\n4. 第234页，第**PRB-192**题中“in”重复出现了两次。\n5. 第236页，第**PRB-194**题将“approached”改为“approaches”，并将“arr”改为“arr001”。\n6. 第247页，第**PRB-210**题将“an”改为“a”。\n7. 第258页，第**PRB-227**题将“A confusion metrics”改为“A confusion matrix”。\n8. 第271页，第**PRB-240**题将“MaxPool2D(4,4,)”改为“MaxPool2D(4,4)”。\n9. 第273页，第**PRB-243**题将“identity”改为“identify”。\n10. 第281页，第**PRB-254**题将“suggest”改为“suggests”。\n11. 第283页，第**PRB-256**题中“happening”拼写错误。\n12. 第286页，“L1, L2”修改为“Norms”。\n13. 第288页，第**SOL-184**题将“the full”改为“is the full”。\n14. 第298页，第**SOL-208**题将“ou1”改为“out”。\n15. 第319页，第**SOL-240**题将“torch.Size([1, 32, 222, 222]).”改为“torch.size([1, 32, 222, 222]).”。\n16. 第283页，第**PRB-256**题中“happening”拼写错误。\n\n**2020年7月12日印刷版及在线版本中的勘误：**\n\n1. 第187页，第**PRB-140**题缺少两张图表（6.3、6.4），在印刷版中未能正确显示。\n![ball001.png](https:\u002F\u002Fimages.squarespace-cdn.com\u002Fcontent\u002Fv1\u002F5c33c435f93fd4233f157b43\u002F1607530038262-PGU5F0YDMFA9NON3NKZA\u002Fball001.png?format=500w)\n\n\n6.3\n\n![ball002.png](https:\u002F\u002Fimages.squarespace-cdn.com\u002Fcontent\u002Fv1\u002F5c33c435f93fd4233f157b43\u002F1607530124438-1U0OIE7QPO0DSMP8LKBS\u002Fball002.png?format=500w)\n\n\n6.4\n**2020年9月21日印刷版及在线版本中的勘误：**\n\n1. 第34页，第**SOL-19**题解答中，0.21886应为0.21305，且0.21886 ± 1.95 × 0.21886应为**0.21305** ± 1.95 × 0.21886。\n2. 第36–37页，第**SOL-21**题解答中，4.8792\u002F0.0258 = **189.116**，而非57.3；同时，pi(33) = 0.01748，而非pi(33) = **0.211868**。\n3. 第49页，第**PRB-47**题中，“专家是**猴子**的概率是多少”应为“专家是**人类**的概率是多少”。\n\n**2020年9月22日印刷版及在线版本中的勘误：**\n\n1. 第73页，第**SOL-56**题解答应为：“Hessian矩阵由**求导**生成”。\n2. 第57页，第**PRB-65**题应为“**两个**神经元”。\n\n**2020年9月24日印刷版及在线版本中的勘误：**\n\n1. 第78页，第**SOL-64**题解答中，OnOffLayer仅当200个神经元中有至少150个关闭时才会关闭。因此，这可以表示为二项分布，该层关闭的概率为：\n![2020-12-24 21_08_52-E__Sync_branded_interviews.ai_amazon_21-12-2020_chap_bayes.tex - TeXstudio.png](https:\u002F\u002Fimages.squarespace-cdn.com\u002Fcontent\u002Fv1\u002F5c33c435f93fd4233f157b43\u002F1608836963860-U8ZD3L5L4IL5QKOAUZD8\u002F2020-12-24+21_08_52-E__Sync_branded_interviews.ai_amazon_21-12-2020_chap_bayes.tex+-+TeXstudio.png?format=750w)","# interviews.ai 快速上手指南\n\n`interviews.ai` 并非传统的软件工具或代码库，而是一本开源的《深度学习面试》专著（Deep Learning Interviews book）。它包含了数百道来自人工智能核心领域的完全解析的面试真题，涵盖信息论、贝叶斯统计、算法微分、逻辑回归及卷积神经网络等主题。\n\n本指南将帮助你快速获取并阅读这份宝贵的学习资源。\n\n## 环境准备\n\n由于本项目主要提供的是 PDF 文档和 LaTeX 源码，无需配置复杂的编程环境或安装依赖包。\n\n*   **系统要求**：Windows、macOS 或 Linux 均可。\n*   **前置依赖**：\n    *   **PDF 阅读器**：如 Adobe Acrobat Reader, Chrome, Edge 或任何主流 PDF 查看软件。\n    *   **可选（仅针对开发者）**：若需编译源码或查看勘误中的图表细节，可安装 LaTeX 发行版（如 TeX Live 或 MiKTeX）及代码编辑器（如 VS Code, TeXstudio）。\n\n## 获取资源\n\n本项目不提供 `pip` 或 `npm` 安装命令，请直接通过以下官方渠道下载电子版。\n\n### 方式一：直接下载 PDF（推荐）\n这是最快捷的方式，适合立即开始阅读和学习。\n\n访问 arXiv 官方页面下载最新版本的 PDF：\n```text\nhttps:\u002F\u002Farxiv.org\u002Fabs\u002F2201.00650\n```\n*注：在页面右侧点击 \"Download: PDF\" 即可保存。*\n\n### 方式二：访问项目主页\n获取更多关于书籍的背景信息、作者联系方式及购买纸质版链接：\n```text\nhttps:\u002F\u002Fwww.interviews.ai\n```\n\n### 方式三：克隆源码仓库（可选）\n如果你需要查看原始的 LaTeX 源码、图片素材或提交勘误（Errata），可以克隆 GitHub 仓库：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002FBoltzmannEntropy\u002Finterviews.ai.git\ncd interviews.ai\n```\n\n## 基本使用\n\n获取 PDF 文件后，即可开始学习。本书专为具有量化背景的数据科学求职者和研究生设计。\n\n### 1. 阅读核心章节\n打开 PDF 文件，建议按照以下核心主题顺序进行研读（第一卷内容）：\n\n*   **Information Theory** (信息论)\n*   **Calculus & Algorithmic Differentiation** (微积分与算法微分)\n*   **Bayesian Deep Learning & Probabilistic Programming** (贝叶斯深度学习与概率编程)\n*   **Logistic Regression** (逻辑回归)\n*   **Ensemble Learning** (集成学习)\n*   **Feature Extraction** (特征提取)\n*   **Deep Learning** (深度学习 - 扩展章节，超过 100 页)\n\n### 2. 学习方法\n本书的特色在于不仅提供答案，还展示了完整的推导过程和推理逻辑。\n*   **刷题模式**：尝试独立解决书中的 \"Problem\" (PRB-xxx)，然后对照 \"Solution\" (SOL-xxx) 检查步骤。\n*   **代码实践**：书中包含大量 PyTorch、Python 和 C++ 的代码示例。建议在本地 IDE 中复现这些代码片段以加深理解。\n\n### 3. 注意事项\n*   **版权许可**：该电子资源仅供**个人学习研究**使用 (Private Study Only)。\n*   **禁止商用**：严禁出售、转售或将内容用于商业目的。\n*   **勘误反馈**：如果在阅读中发现错误，可参考仓库中的 `Errata` 部分，或通过 LinkedIn 联系作者 (Amir Ivry 或 Shlomo Kashani) 进行反馈。\n\n---\n*引用本书学术格式：*\n```bibtex\n@misc{kashani2021deep,\n      title={Deep Learning Interviews: Hundreds of fully solved job interview questions from a wide range of key topics in AI}, \n      author={Shlomo Kashani and Amir Ivry},\n      year={2021},\n      eprint={2201.00650},\n      note = {ISBN 13: 978-1-9162435-4-5 }, \n      url = {https:\u002F\u002Fwww.interviews.ai}, \n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n```","一名拥有量化背景的硕士研究生正在备战某头部科技公司的深度学习算法岗面试，面对涵盖信息论、贝叶斯统计到卷积神经网络等广泛考点的压力，急需系统化的复习方案。\n\n### 没有 interviews.ai 时\n- **知识盲区难以自查**：复习范围零散，无法确定是否覆盖了从算法微分到感知机等核心考点的深层细节，容易在冷门知识点上失分。\n- **缺乏实战解题思路**：手头只有理论教材，面对面试官提出的“完全求解”类问题时，不知道如何构建逻辑严密的推导过程。\n- **表达训练不足**：虽然理解原理，但缺乏将复杂数学概念转化为引人入胜的技术故事的能力，导致面试沟通显得干瘪生硬。\n- **焦虑感加剧**：面对竞争激烈的求职环境，因缺乏高质量的模拟题库而对自己能否通过技术面感到极度不确定。\n\n### 使用 interviews.ai 后\n- **核心考点全覆盖**：利用书中连贯的课程体系，系统梳理了信息论、逻辑回归等关键领域，确保知识网络无死角。\n- **掌握标准解题范式**：通过研习数百道全解面试题，学会了如何一步步拆解高难度问题，并能独立复现完整的推导逻辑。\n- **提升技术叙事能力**：借鉴书中将枯燥问题融入引人深思的故事框架，能够在面试中流畅、生动地阐述技术方案，展现专业深度。\n- **建立应试自信**：依托针对硕士及博士水平设计的高强度习题进行演练，显著提升了应对高压面试的心理素质和实战能力。\n\ninterviews.ai 不仅是一本习题集，更是连接学术理论与职场实战的桥梁，帮助求职者将深厚的量化背景转化为斩获 Offer 的关键竞争力。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FBoltzmannEntropy_interviews.ai_36870c2f.png","BoltzmannEntropy","Solomon","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FBoltzmannEntropy_b6089677.png","Book author: \"Deep Learning Interviews\". \r\nQuantum Computing, Diffusion Models, LLM's, Generative AI. https:\u002F\u002Fqneura.ai\u002F",null,"https:\u002F\u002Fqneura.ai\u002F","https:\u002F\u002Fgithub.com\u002FBoltzmannEntropy",4823,322,"2026-04-08T03:26:44",1,"","未说明",{"notes":86,"python":84,"dependencies":87},"该项目并非可运行的 AI 软件工具，而是一本名为《Deep Learning Interviews》的书籍（PDF 电子书）及其相关资源。内容包含深度学习面试题、解答及少量 PyTorch\u002FPython\u002FC++ 代码示例，主要用于个人学习和面试准备，无需安装特定的运行环境或依赖库。",[],[16,89,14],"其他",[91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109],"data-science","machine-learning","deep-learning","interview-preparation","jobs","artificial-intelligence","pytorch-tutorial","graduate-school","pytorch","python","jax","autograd","information-theory","bayesian-statistics","convolutional-neural-networks","ensemble-learning","feature-extraction","logistic-regression","loss-functions","2026-03-27T02:49:30.150509","2026-04-09T05:23:03.762224",[113,118],{"id":114,"question_zh":115,"answer_zh":116,"source_url":117},25580,"在 iPad 的 iBooks 应用中打开 PDF 时提示“密码保护”无法阅读，如何解决？","这通常是特定设备或软件版本的问题。维护者确认该 PDF 文件本身没有密码保护，在 Android、PC 以及较新的 iPad 设备上均可正常打开并导出到 iBooks。如果您使用的是旧款 iPad（如 iPad mini 2）或旧版 iOS（如 iOS 10），建议尝试在其他设备上查看，或更新系统及阅读软件后再试。","https:\u002F\u002Fgithub.com\u002FBoltzmannEntropy\u002Finterviews.ai\u002Fissues\u002F1",{"id":119,"question_zh":120,"answer_zh":121,"source_url":122},25581,"这本书的第二卷（Volume 2）预计何时发布？","目前作者明确表示没有发布第二版或第二卷的计划。","https:\u002F\u002Fgithub.com\u002FBoltzmannEntropy\u002Finterviews.ai\u002Fissues\u002F12",[]]