oaxino Posted October 15, 2024 Report Share Posted October 15, 2024 Master Llm Optimization: Boost Ai Performance & EfficiencyPublished 10/2024MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHzLanguage: English | Size: 1.89 GB | Duration: 3h 4mUnlock advanced techniques for fine-tuning, scaling, and optimizing LLMs to enhance AI capabilitiesWhat you'll learnLearn to use Google Colab for unleashing the power of Python's text analysis and deep learning ecosystemIntroduction to the basic concepts around LLMs and Generative AIGet acquainted with common Large Language Model (LLM) frameworks including LangChainLearning about using the Hugging Face hub for accessing different LLMsIntroduction to the theory and implementation of LLM OptimizationRequirementsPrior experience of using Jupyter notebooksPrior exposure to Natural Language Processing (NLP) concepts will be helpful but not compulsoryAn interest in using Large Language Models (LLMs) for your own documentsDescriptionMaster LLM Optimization: Boost AI Performance & EfficiencyUnlock the power of Large Language Models (LLMs) with our cutting-edge course, "Master LLM Optimization: Boost AI Performance & Efficiency." Designed for AI enthusiasts, data scientists, and developers, this course offers an in-depth journey into LLMs, focusing on optimization techniques that elevate AI capabilities. Whether you're a beginner in LLM implementation or an experienced practitioner seeking to refine your skills, this course equips you with the knowledge and tools to excel in this rapidly evolving field.Course Overview:This course deep dives into LLM frameworks like OpenAI, LangChain, and LLAMA-Index, empowering you to build and fine-tune AI solutions like Document-Reading Virtual Assistants. With a comprehensive curriculum, you'll explore the theory and practical implementation of LLM optimization, gaining hands-on experience with popular LLM models like GPT and Mistral through Hugging Face. By the end of the course, you'll have mastered advanced techniques for harnessing LLMs, enabling you to develop AI systems that are both efficient and powerful.Key Learning Outcomes:Foundations of Generative AI and LLMs: Understand the core concepts of Gen AI and LLMs, laying a solid foundation for more advanced topics.Introduction to LLM Frameworks: Get hands-on experience with popular LLM frameworks, including OpenAI, LangChain, and LLAMA-Index, enabling you to build and deploy AI applications with ease.Accessing LLM Models: Learn how to access LLM models via Hugging Face, work with cutting-edge models like Mistral, and implement them effectively.LLM Optimization Techniques: Discover advanced optimization methods such as quantization, fine-tuning, and scaling, essential for enhancing LLM performance in real-world applications.Retrieval-Augmented Generation (RAG): Gain insights into RAG and its role in LLM optimization, enabling more accurate and efficient AI responses.Leveraging LLM Tools for Summarization & Querying: Master using LLM tools for abstract summarization and querying, ensuring you can harness the full potential of large language models.Why Enroll?Guided by an expert instructor with an MPhil from the University of Oxford and a data-intensive PhD from Cambridge University, this course offers unparalleled expertise in LLM optimization. You'll benefit from a supportive learning environment, practical assignments, and a community of AI enthusiasts, ensuring a comprehensive understanding of LLM implementation.Ready to Become an LLM Expert?Enrol now to transform your AI capabilities, master LLM optimization techniques, and unlock the potential of text data with large language models. Join us and elevate your expertise in AI today!OverviewSection 1: IntroductionLecture 1 IntroductionLecture 2 Data and CodeLecture 3 What is Google Colab?Lecture 4 Google Colabs and GPULecture 5 Installing Packages In Google ColabLecture 6 Read in a PDFLecture 7 Read in Multiple PDFsSection 2: Welcome to the World of Gen-AI and LLMsLecture 8 Lowdown on GenAI ModelsLecture 9 More on Gen-AILecture 10 How Does Gen AI WorkLecture 11 What are GPTs?Lecture 12 Interplays Between Gen-AI and LLMsLecture 13 Introduction to Open APILecture 14 Other LLMsLecture 15 Start With Hugging FaceLecture 16 Access and Use Other LLMs Via Hugging FaceLecture 17 Access Mistral LLM With Hugging FaceLecture 18 LLMs on Google Cloud Computing (GCP)Section 3: Start With Large Language Models (LLMs)Lecture 19 LLM WorkflowLecture 20 Overview of SummarizationLecture 21 Abstract SummarizationLecture 22 Langchain TechLecture 23 Langchain QALecture 24 Introduction to LlamaLecture 25 Llama- Another LLM ImplementationSection 4: Introduction to Prompt EngineeringLecture 26 Get PromptingLecture 27 More PromptingSection 5: LLM Optimisation- An OverviewLecture 28 LLM Optimisation-TheoryLecture 29 Basic Quantisation- A Quick ImplementationLecture 30 Stochastic Gradient Descent (SGD) For LLMs-TheoryLecture 31 SGD Implementation For LLM OptimisationLecture 32 RAGs and Their Roles in LLM Optimisation- TheoryLecture 33 A RAG WorkflowLecture 34 Prepare The External Text Data For Use in RAGLecture 35 Create and Retrieve EmbeddingsLecture 36 RetrievalLecture 37 More Detailed QueriesSection 6: MiscallaneousLecture 38 Gen AILecture 39 Go Home- You Are DrunkLecture 40 Another Jupyter OptionLecture 41 Memory ManagementStudents with prior exposure to NLP analysis,Those interested in using LLM frameworks for learning more about your texts,Students and practitioners of Artificial Intelligence (AI)ScreenshotsSay "Thank You"rapidgator.net:https://rapidgator.net/file/e6efea6fce9d411deeec3216ef4593e1/pkyyx.Master.Llm.Optimization.Boost.Ai.Performance..Efficiency.part1.rar.htmlhttps://rapidgator.net/file/9f64059d07d623b791bcf0ea933101a6/pkyyx.Master.Llm.Optimization.Boost.Ai.Performance..Efficiency.part2.rar.htmlddownload.com:https://ddownload.com/rmagdvnvpv8u/pkyyx.Master.Llm.Optimization.Boost.Ai.Performance..Efficiency.part1.rarhttps://ddownload.com/pby648yzk0dk/pkyyx.Master.Llm.Optimization.Boost.Ai.Performance..Efficiency.part2.rar Link to comment Share on other sites More sharing options...
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