Genai World : Llm, Fine-Tuning, Rag & Prompt Engineering

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What you'll learn

Understand the fundamentals of prompting in the context of large language models (LLMs).

Learn the importance of prompt engineering for optimizing model perform

Explore advanced concepts like Direct Preference Optimization (DPO) and Parameter-Efficient Fine-Tuning (PEFT).

Gain insights into Retrieval Augmented Generation (RAG), understanding its components and how it enhances LLM capabilities.

Requirements

Yes, students should have: A foundational understanding of artificial intelligence and machine learning concepts, especially related to language models. Proficiency in Python programming, as the course includes detailed code examples and exercises. Familiarity with deep learning frameworks. Basic knowledge of natural language processing (NLP) and transformer models. Access to necessary computational resources, such as a GPU-enabled environment.

Description

This course covers everything from Large Language Models (LLMs), prompt engineering to parameter-efficient fine-tuning (PEFT) and advanced concepts like Direct Preference Optimization (DPO). You’ll also dive deep into Retrieval Augmented Generation (RAG) to enhance your LLMs' capabilities by integrating retrieval systems for superior responses.By the end of this course, you’ll be equipped to create AI solutions that align perfectly with human intent and outperform standard models.                                                  What You’ll Learn:Craft powerful and effective prompts for LLMs to optimize outputs.Master Direct Preference Optimization (DPO) and PEFT for domain-specific fine-tuning.Implement Retrieval Augmented Generation (RAG) to elevate model performance.Gain insights into state-of-the-art LLM capabilities, focusing on practical and advanced techniques.Develop customized solutions with hands-on code examples and exercises.     What you will Get A foundational understanding of artificial intelligence and machine learning concepts, especially related to language models. Proficiency in Python programming, as the course includes detailed code examples and exercises. Familiarity with deep learning frameworks. Basic knowledge of natural language processing (NLP) and transformer models. Access to necessary computational resources, such as a GPU-enabled environment. In addition to the core topics, our course also features real-world case studies on fine-tuning, prompt engineering, and Retrieval Augmented Generation (RAG). These case studies offer practical, hands-on insights into how these techniques are applied in real AI projects .These case studies provide a practical framework for applying the theoretical concepts covered in the course, helping learners implement these methods in their own projects.                     

Overview

Section 1: Welcome

Lecture 1 Why Generative AI? Our Vision and Purpose for Offering this Course

Lecture 2 Unlock AI Mastery: What You’ll Gain From Our Generative AI Course

Section 2: Introduction

Lecture 3 Introduction

Lecture 4 Training Large Language Models

Lecture 5 Factors Influencing LLM Performance

Lecture 6 LLM Use Cases

Lecture 7 Challenges Facing LLMs

Lecture 8 Domain-Specific Pre-Training

Lecture 9 Domain-Specific Fine-Tuning

Lecture 10 Choosing the Right Adaptation Method

Lecture 11 Using LLMs Effectively

Lecture 12 Benefits of Domain-Specific LLMs

This course is ideal for: Machine learning engineers and data scientists looking to enhance their skills in fine-tuning large language models. AI researchers and practitioners interested in advanced techniques like RAG, PEFT, and QLoRA. Developers and programmers aiming to implement AI solutions that require domain-specific model customization. Students and academics studying artificial intelligence, machine learning, or natural language processing. Anyone interested in state-of-the-art AI technologies and how to apply them effectively in real-world scenarios.