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所在平台: Udemy |
课程主页: https://www.udemy.com/course/llm-genai-interview-questions-and-answers-basic-to-expert/
课程评论：没有评论
课程名称：掌握大语言模型（LLM）和生成式人工智能（Gen AI）：600+真实面试问题[2025] 课程概述： 本课程包含600个领先科技公司最常问的真实面试问题，旨在帮助学员掌握大语言模型（LLMs）和生成式人工智能的奥秘。课程“LLM和GenAI面试问题与答案：基础到专家”是您深入理解现代人工智能系统概念、技术和挑战的终极指南，帮助您在面试中脱颖而出，推动自己的人工智能和机器学习职业发展。 课程内容： 本课程适合从初学者到有志成为专家的所有人，涵盖从基础知识到高级概念的全面学习，涉及关键主题如提示工程、模型微调、变换器架构、伦理AI考虑及实际应用等。课程采用互动形式，包含顶尖科技公司提出的真实面试问题及其详细解答，帮助学员建立自信。通过动手练习和情景问题解决，学员将培养与行业需求直接相关的实用技能。 学习目标： - 掌握从变换器架构到分词的关键概念 - 深入了解诸如多头注意力、残差连接和混合精度训练等高级主题 - 通过解决从基础到专家级的面试问题，确保充分准备应对真实世界的挑战 示例问题： 1. 在有限内存的设备上部署经过微调的大语言模型时，模型的完整大小超过设备容量。您将采用什么策略使部署可行？ A) 通过去除整个层来修剪模型。 B) 压缩模型，通过量化所有参数。 C) 使用PEFT进行微调并仅部署适应的参数与预训练模型一起。 D) 减少模型使用的词汇大小。 2. 在微调大模型的过程中，您观察到大部分参数的梯度接近零，导致收敛缓慢。您将如何解决这个问题？ A) 增加训练轮数以实现更好的收敛。 B) 应用LoRA专注于低秩组件的适应。 C) 实施梯度裁剪以控制大更新。 D) 使用更大的学习率来加速收敛。 适合人群： 如果您对机器学习有基本理解，本课程将帮助您提升到一个新层次。无论您是学生、求职者还是希望提升技能的专业人士，这门课程都适合您。 结尾： 不要错过这个提升您人工智能职业生涯的机会！立即注册“LLM和Gen AI面试问题：基础到专家”课程，迈出成为人工智能专家的第一步！您的未来在人工智能领域从今天开始。快来点击“立即注册”，开启您的成功之旅！
This course features 600 Real and Most Asked Interview Questions that leading tech companies have asked. Unlock the secrets of Large Language Models (LLMs) and Generative AI with our expertly designed course, "LLM & GenAI Interview Questions and Answers: Basic to Expert" This is your ultimate guide to mastering the concepts, techniques, and challenges of modern AI systems, helping you stand out in interviews and advance your career in AI and machine learning.Are you ready to dominate your next interview in the exciting world of Large Language Models (LLMs) and Generative AI? Look no further! This course is your ultimate guide to mastering LLMs and cracking interviews, whether you're a beginner or an aspiring expert. This course takes you on a journey from foundational knowledge to advanced concepts, covering key topics such as prompt engineering, model fine-tuning, transformer architecture, ethical AI considerations, and real-world applications. Whether you're preparing for technical interviews or just want to deepen your understanding of cutting-edge AI technologies, this course has you covered.Our interactive format includes real interview questions asked by leading tech companies, along with detailed answers and explanations to build your confidence. Through hands-on exercises and scenario-based problem-solving, you'll develop practical skills that are directly applicable to industry demands.Designed for aspiring data scientists, software engineers, and AI enthusiasts, this course is a must-have resource to sharpen your expertise and stay ahead in the rapidly evolving AI landscape. Enroll now and take the first step toward becoming an AI expert ready to tackle any interview challenge!Why Enroll in This Course?Imagine walking into an interview with full confidence, armed with in-depth knowledge and hands-on practice. In this course, you'll: Master key concepts from Transformer Architecture to Tokenization. Dive deep into advanced topics like Multi-Head Attention, Residual Connections, and Mixed-Precision Training. Learn by solving basic to expert-level interview questions, ensuring you're fully prepared for real-world challenges. What Will You Learn? Here's just a sneak peek at what's waiting for you: 1️. Transformer Architecture - The backbone of LLMs. 2️. Attention Mechanism - Powering breakthroughs in AI. 3️. Positional Encoding - Understanding how sequences matter. 4️. Tokenization - Decoding text like a pro. ...and so much more! Sample Questions:1. You need to deploy a fine-tuned large language model on a device with limited memory. The full model size exceeds the device's capacity. What strategy would you use to make deployment feasible?A) Prune the model by removing entire layers. B) Compress the model by quantizing all parameters. C) Fine-tune using PEFT and deploy only the adapted parameters alongside the pre-trained model. D) Reduce the vocabulary size used by the model.2. During the fine-tuning of a large model, you observe that the gradients of most parameters are close to zero, resulting in slow convergence. How would you address this issue?A) Increase the number of epochs to allow for better convergence. B) Apply LoRA to focus the adaptation on low-rank components. C) Implement gradient clipping to control large updates. D) Use a larger learning rate to accelerate convergence.By the End of This Course: You'll confidently tackle advanced LLM concepts like Perplexity and Mixed-Precision Training and understand cutting-edge AI techniques that top companies are looking for. Who Should Join? If you have a basic understanding of Machine Learning, this course will take you to the next level. Whether you're a student, a job seeker, or a professional looking to upskill, this course is for YOU. Take Action Now! Don't miss this chance to supercharge your career in AI. Join me on this exciting journey to master LLMs and Generative AI! Enroll now in the "LLM and Gen AI Interview Questions: Basic to Expert" course on Udemy. Your future in AI starts today! Click "Enroll Now" and take the first step toward your success!Enroll today and equip yourself with the knowledge and practice needed to succeed in the world of Large Language Models (LLMs) Programming by mastering real questions that leading tech companies have asked.
