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Master Llm Optimization: Boost Ai Performance & Efficiency - mitsumi - 10-15-2024
Master Llm Optimization: Boost Ai Performance & Efficiency
![[Image: d81367ad6633a76d30d452c1a3a434cd.jpeg]](https://i124.fastpic.org/big/2024/1015/cd/d81367ad6633a76d30d452c1a3a434cd.jpeg)
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.89 GB | Duration: 3h 4m
Unlock advanced techniques for fine-tuning, scaling, and optimizing LLMs to enhance AI capabilities
What you'll learn
Learn to use Google Colab for unleashing the power of Python's text analysis and deep learning ecosystem
Introduction to the basic concepts around LLMs and Generative AI
Get acquainted with common Large Language Model (LLM) frameworks including LangChain
Learning about using the Hugging Face hub for accessing different LLMs
Introduction to the theory and implementation of LLM Optimization
Requirements
Prior experience of using Jupyter notebooks
Prior exposure to Natural Language Processing (NLP) concepts will be helpful but not compulsory
An interest in using Large Language Models (LLMs) for your own documents
Description
Master 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!
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Data and Code
Lecture 3 What is Google Colab?
Lecture 4 Google Colabs and GPU
Lecture 5 Installing Packages In Google Colab
Lecture 6 Read in a PDF
Lecture 7 Read in Multiple PDFs
Section 2: Welcome to the World of Gen-AI and LLMs
Lecture 8 Lowdown on GenAI Models
Lecture 9 More on Gen-AI
Lecture 10 How Does Gen AI Work
Lecture 11 What are GPTs?
Lecture 12 Interplays Between Gen-AI and LLMs
Lecture 13 Introduction to Open API
Lecture 14 Other LLMs
Lecture 15 Start With Hugging Face
Lecture 16 Access and Use Other LLMs Via Hugging Face
Lecture 17 Access Mistral LLM With Hugging Face
Lecture 18 LLMs on Google Cloud Computing (GCP)
Section 3: Start With Large Language Models (LLMs)
Lecture 19 LLM Workflow
Lecture 20 Overview of Summarization
Lecture 21 Abstract Summarization
Lecture 22 Langchain Tech
Lecture 23 Langchain QA
Lecture 24 Introduction to Llama
Lecture 25 Llama- Another LLM Implementation
Section 4: Introduction to Prompt Engineering
Lecture 26 Get Prompting
Lecture 27 More Prompting
Section 5: LLM Optimisation- An Overview
Lecture 28 LLM Optimisation-Theory
Lecture 29 Basic Quantisation- A Quick Implementation
Lecture 30 Stochastic Gradient Descent (SGD) For LLMs-Theory
Lecture 31 SGD Implementation For LLM Optimisation
Lecture 32 RAGs and Their Roles in LLM Optimisation- Theory
Lecture 33 A RAG Workflow
Lecture 34 Prepare The External Text Data For Use in RAG
Lecture 35 Create and Retrieve Embeddings
Lecture 36 Retrieval
Lecture 37 More Detailed Queries
Section 6: Miscallaneous
Lecture 38 Gen AI
Lecture 39 Go Home- You Are Drunk
Lecture 40 Another Jupyter Option
Lecture 41 Memory Management
Students 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)
Screenshots
![[Image: 0c86ad973cd016938c813b285719da5c.jpeg]](https://i124.fastpic.org/big/2024/1015/5c/0c86ad973cd016938c813b285719da5c.jpeg)
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