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所在平台: Udemy |
课程主页: https://www.udemy.com/course/llm-gen-ai-engineer-interview-questions-with-explanation/
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课程名称：大型语言模型与生成AI面试问题（附解释） 课程概述：本课程精心设计的练习测试紧跟AI行业的最新进展，全面涵盖了关键主题，包括大型语言模型（LLM）如GPT和LLama的模型架构、LLM预训练、以及LLM微调技术（如LORA、BERT、DistilBERT、CLIP等）。课程还涉及Hugging Face库、变换器架构、注意机制、模型压缩技术（如知识蒸馏和量化）、扩散模型、多模态模型、提示工程、检索增强生成（RAG）系统、嵌入模型、向量数据库等。此外，课程提供了来自领先科技公司的真实面试问题。 示例问题包括： 1. 注意掩码在变换器模型中的作用是什么？ 2. RoBERTa如何与BERT在训练期间处理令牌掩码的不同？ 3. BERT中的Q、K和V矩阵的维度如何确定？ 4. 如何在知识蒸馏过程中使用温度缩放？ 5. 对于具备1024嵌入尺寸和24层的BERT模型，如果词汇表大小为50000，嵌入层的参数量为多少？ 6. LangChain代理如何与外部数据库互动？ 7. transformers.DataCollatorForLanguageModeling的用途是什么？ 8. GAN中的鉴别器架构通常与生成器有何不同？ 9. LangChain中的conditional_prompt方法的目的是什么？ 10. RAG系统如何有效处理模糊查询？ 通过我们的动态课程“LLM与生成AI工程师面试问题（附解释）”，您将全面准备生成AI和大型语言模型工程师的面试。课程中还涉及测试LLM与生成AI解决方案的概念性和实际实施问题，使用PyTorch和TensorFlow框架，确保您能够应对面试中的任何技术挑战。 该课程每月更新，新增100多个问题，以反映LLM和生成AI模型不断变化的格局。 课程涵盖的主题包括： - 变换器与大型语言模型（GPT、LLama、BERT）的模型架构 - Hugging Face变换器库 - 模型压缩技术 - 量化与知识蒸馏 - LLM模型的预训练、微调与对齐技术 - PEFT、LORA、RLHF、DPO、PPO - 嵌入模型 - 扩散模型 - 视觉语言模型 - 多模态模型 - 检索增强生成系统（RAG） - LangChain - 向量数据库 - LLM模型部署 - LLM模型评估指标 - 分布式LLM模型训练
Our meticulously designed practice tests keeps pace with the AI industry's latest advancements, covers both depth and breath, while concentrating on the important topics including Model Architectures of LLM Models like GPT, LLama, LLM Pretraining, LLM fine-tuning techniques like LORA, BERT Model, DistilBERT, CLIP, Hugging Face library, Transformers Architecture, Attention Mechanism, Model Compression techniques such as Knowledge Distillation and Quantization, Diffusion Models, Multimodal models, Prompt Engineering, Retrieval Augmented Generation (RAG) Systems, Embedding Models, Vector Databases and more. Additionally, the course features real questions that have been asked by leading tech companies.Sample Questions:1. What is the role of an attention mask in Transformer models?2. How does RoBERTa handle token masking differently than BERT during training?3. How are the dimensions of the Q, K, and V matrices determined in BERT?4. How can temperature scaling be used in the knowledge distillation process?5. For a BERT model with an embedding size of 1024 and 24 layers, how many parameters are in the embedding layer if the vocabulary size is 50,000?6. How do LangChain agents interact with external databases?7. What is the transformers.DataCollatorForLanguageModeling used for?8. How does the discriminator's architecture typically compare to the generator's in a GAN?9. What is the purpose of the conditional_prompt method in LangChain?10. How can RAG systems handle ambiguous queries effectively?Prepare comprehensively for Generative AI and Large Language Models (LLM) Engineer interviews with our dynamic Udemy course, "LLM & Gen AI Engineer Interview Questions (with Explanation)"You'll also delve into questions that test conceptual and practical implementation of LLM & Gen AI based solutions using PyTorch and TensorFlow Frameworks, ensuring you're well-prepared to tackle any technical challenge in your interview.This course evolves every month with 100+ NEW questions added every month to reflect the ever-changing landscape of LLMs and Generative AI Models.Topics Covered in the Course:-Model Architectures of Transformer & LLM Models like GPT, LLama, BERTHugging Face Transformers LibraryModel Compression Techniques - Quantization & Knowledge DistillationLLM Model - Pretraining, Fine-tuning & Alignment Techniques - PEFT, LORA, RLHF, DPO, PPOEmbedding Models Diffusion ModelsVision Language ModelsMultimodal Models Retrieval Augmented Generation Systems (RAGs) - LangChainVector DatabasesLLM Model DeploymentLLM Model Evaluation MetricesDistributed LLM Model Training
