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In this video on LLM Benchmarking, we will learn about LLM Benchmarking, where we explore how one large language model (LLM) is tested against another. We'll break down the key metrics and evaluation methods used to compare LLMs, such as accuracy, performance on various tasks, response quality, and more. Whether you're curious about how AI models like GPT, Claude, or LLama are ranked, or you're looking to understand the benchmarking process that drives the development of cutting-edge language models, this video has you covered.
00:00 Introduction to LLM Benchmarking
02:21 What Is LLM Benchmarking?
03:12 How LLM Benchmarking work?
04:40 Key Metrices For LLM Benchmarking
06:12 Limitations for LLM Benchmarking
07:17 LLM Leaderboard
✅ What are LLM benchmarks?
LLM benchmarks are standardized tests and metrics used to evaluate and compare the performance of large language models (LLMs) across various tasks.
✅ How to make your own LLM benchmark?
To create an LLM benchmark, define the tasks you want to evaluate, select relevant metrics (e.g., accuracy, fluency), and gather a representative dataset.
✅ What does the LLM model stand for?
LLM stands for Large Language Model, which refers to a type of artificial intelligence model designed to understand, generate, and manipulate human language.
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➡️ About Applied Generative AI Specialization
Master Generative AI with this cutting-edge Applied AI Course by Purdue University Online and Simplilearn. Explore prompt engineering, large language models, attention mechanisms, RAG, and LLM fine-tuning. Learn in-demand tools and shape the future of intelligent systems.
Key Features
✅ Program completion certificate from Purdue University Online and Simplilearn
✅ Access to Purdue’s alumni association membership on program completion
✅ 50+ hours of core curriculum delivered in live online classes by industry experts
✅ Build Generative AI-enabled applications through hands-on projects
✅ Live online masterclasses delivered by Purdue faculty and staff
✅ Gain exposure to Copilot, Azure AI Studio, ChatGPT, OpenAI, Dall-E 2, Hugging Face & other prominent tools
✅ Explore concepts like prompt engineering, attention mechanism, transformers, LLM application development, Retrieval Augmented Generation (RAG), and LLM fine-tuning
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies
✅ Course completion certificate hosted on the Microsoft Learn portal
✅ Build an end-to-end RAG-based application through hands-on projects
Learning Path
✅ AGS: Program Induction
✅ AGS: Python Basics (Optional)
✅ AGS: Essentials of Generative AI, Prompt Engineering & ChatGPT
✅ AGS: Advanced Generative AI - Models and Architecture
✅ AGS: Advanced Generative AI - Building LLM Applications
✅ AGS: Advanced Generative AI - Image Generation Capabilities
✅ AGS: Generative AI Governance
Electives:
✅ AGS: Microsoft Azure AI Fundamentals - Generative AI
✅ AGS: Academic Masterclass
✅ AGS: Microsoft Copilot Foundations
Skills Covered
✅ Python Programming
✅ Explainable AI
✅ Prompt Engineering
✅ Variational Autoencoders VAEs
✅ Generative Adversarial Networks GANs
✅ TransformersLLM Architecture
✅ Retrieval Augmented Generation RAG
✅ Langchain for Workflow Design
✅ GenAI Application Development
✅ LLM Fine Tuning
✅ LLM Benchmarking
✅ Stable Diffusion
✅ Generative AI Governance
✅ Attention Mechanism
Tools:
✅ Python
✅ ChatGPT
✅ OpenAI
✅ HuggingFace
✅ Gemini
✅ CoPilot
✅ Dall-E-2
✅ LangChain
✅ Gradio
✅ Chroma
✅ Streamlit
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