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Building and Fine-Tuning LLM Applications · LearnSpace
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Building and Fine-Tuning LLM Applications

Курс от Packt
Продвинутый≈ 10 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this comprehensive course, you'll learn how to build and fine-tune large language models (LLMs) for real-world applications. Starting with fundamental concepts, you'll progress through hands-on projects that focus on document-based retrieval-augmented generation (RAG) systems, LangChain integration, and fine-tuning techniques. You'll gain the skills to build custom applications like a PDF RAG system, a voice assistant, and a YouTube video summarizer, with a focus on optimizing the retrieval and generation of content. With a blend of theoretical lessons and practical exercises, this course ensures you master both building and fine-tuning LLMs for various AI-driven tasks. You'll also dive deep into advanced fine-tuning methods like LoRA (Low-Rank Adaptation), learning to fine-tune models efficiently with minimal computational resources. Throughout the course, you'll implement real-world projects that integrate sophisticated LLM functionalities into usable applications. By the end of the course, you’ll be capable of deploying and fine-tuning LLMs for personalized tasks, giving you the tools to tackle complex AI challenges in your own projects. This course is designed for intermediate to advanced learners with prior programming experience. It’s perfect for those who want to deepen their understanding of LLMs and apply them to solve industry-specific problems. No prior experience with fine-tuning is required, though knowledge of Python and machine learning basics will be beneficial. By the end of the course, you will be able to build, fine-tune, and deploy LLM applications, including RAG systems, voice assistants, and specialized chatbots, using advanced techniques such as LoRA fine-tuning.

Навыки, которые вы освоите

Fine-tuningModel DeploymentRetrieval-Augmented GenerationApplied Machine LearningAPI TestingModel OptimizationAI WorkflowsApplication Deployment

Программа курса

6 модулей · 61 учебных материалов

01Hands-On: PDF RAG System with Text Chunking9 материалов

Hands-On: PDF RAG System with Text Chunking

Introduction to the Course 'Building and Fine-Tuning LLM Applications'ЧтениеFull Specialization ResourcesЧтениеPDF RAG Workflow: Architecture OverviewВидеоPDF and Chunk Processing and Chunk Overlap: Deep DiveВидео

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Packt - Course Instructors

Преподаватель курса

Building and Fine-Tuning LLM Applications
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Обучение на Coursera

≈ 10 ч

6 модулей

Язык: Английский

Часть программы вашего университета
Setting Up the SimpleRAGSystem Class and MethodsВидео
Testing the PDF RAG SystemВидео
Simple PDF RAG Workflow: SummaryВидео
Understanding Document Chunking and Embedding in Retrieval SystemsDIALOGUE
Hands-On: PDF RAG System with Text Chunking - AssessmentЗадание
02LangChain Fundamentals and Workflow Integration16 материалов

LangChain Fundamentals and Workflow Integration

LLM Frameworks Introduction: LangChain FundamentalsВидеоWhat Is LangChain and Main ComponentsВидеоLangChain Setup and ChatModelВидеоHands-On: LangChain ChatPromptTemplatesВидеоIndexes, Retrievers, and Data Preparation: OverviewВидеоHands-On: LangChain TextLoadersВидеоHands-On: Text Splitting and CleaningВидеоHands-On: Embeddings and Retriever with FAISS VectorStoreВидеоLangChain TextSplitter: Deep DiveВидеоLangChain DirectoryLoaderВидеоLangChain PDFLoaderВидеоHands-On: LangChain ChainsВидеоHands-On: Simple RAG System with Chat and LangChain ChainsВидеоHands-On: Full RAG System QA Bot Using LangChainВидеоUnderstanding and Using LangChain for Building LLM ApplicationsDIALOGUELangChain Fundamentals and Workflow Integration - AssessmentЗадание
03Hands-On: Building LLM Applications with LangChain11 материалов

Hands-On: Building LLM Applications with LangChain

LLM Application: News Summarizer—Architectural OverviewВидеоNews Summarizer: Full ImplementationВидеоLLM Application: YouTube Video Summarizer—Architectural OverviewВидеоYouTube Video Summarizer and Q&A Dependency SetupВидеоYouTube Video Summarizer Class Setup and WalkthroughВидеоYouTube Video Summarizer Q&A: Testing the WorkflowВидеоLLM Application: Voice Assistant RAG System—Architectural OverviewВидеоVoice Assistant RAG System: DemoВидеоVoice Assistant RAG System: Walkthrough and DemoВидеоDesigning an Interactive Coding Exercise on Content Summarization with LLMsDIALOGUEHands-On: Building LLM Applications with LangChain - AssessmentЗадание
04Fine-Tuning LLMs10 материалов

Fine-Tuning LLMs

Fine-Tuning Introduction: OverviewВидеоFine-Tuning Techniques: OverviewВидеоFine-Tuning Comparison of TechniquesВидеоFine-Tuning General Process: OverviewВидеоFine-Tuning OpenAI Models PricingВидеоTokens and the Tokenizer OpenAI ToolВидеоHands-On: Fine-Tuning an OpenAI Model—Full WalkthroughВидеоCreating a Chatbot with Our Fine-Tuned Model and TestingВидеоUnderstanding Fine Tuning of Language ModelsDIALOGUEFine-Tuning LLMs - AssessmentЗадание
05LoRA-Based Fine-Tuning and Deployment11 материалов

LoRA-Based Fine-Tuning and Deployment

LoRA Introduction: BenefitsВидеоLoRA Deep AnalysisВидеоLoRA Implementation Strategy WorkflowВидеоHands-On: Training Models—LoRA and PEFTВидеоRunning LoRA Model Fine-Tuning and TestingВидеоCreating an API Service to Interface with Our Fine-Tuned ModelsВидеоTesting Our LoRA Model API EndpointВидеоChatting with LoRA Fine-Tuned ModelsВидеоFull LoRA Workflow: Train and Chat with Fine-Tuned ModelsВидеоUnderstanding Parameter Efficient Fine-Tuning with LoRADIALOGUELoRA-Based Fine-Tuning and Deployment - AssessmentЗадание
06Wrap-Up and Next Steps4 материалов

Wrap-Up and Next Steps

Conclusion to the SpecializationВидеоConclusion to the Course 'Building and Fine-Tuning LLM Applications'ЧтениеFull Course Practice AssessmentЗаданиеFull Course AssessmentЗадание