Master Llm Optimization: Boost Ai Performance & Efficiency

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)