Generative Ai - Llm And Beyond

LLM Lifecycle, Prompt Engineering, LLM Properties, Fine-tuning, PEFT LORA, RLHF, RAG, PPO,DPO,ORPO, AI for Vision

What you'll learn

LLAMA 2

CHATGPT

LARGE LANGUAGE MODEL

PROMPT ENGINEERING

LLM FINE TUNING

RAG

RLHF

LLM USE CASES

LLM BASICS

LLM FOR EVERYONE

LLM Based chatbot

chatbot

Instruction fine tuning

in context learning

few shot inference

hallucination

Reinforcement learning from human feedback

Retrieval Augmentation Generation

Tools for reasoning

Agents

Augmentation

Automation

Transformers

GEN-AI

GENERATIVE AI

ARTIFICIAL INTELLIGENCE

DATA SCIENCE

MACHINE LEARNING

DEEP LEARNING

LANGCHAIN

LAMMAINDEX

Low-Rank Adaptation

LORA

METRICS

PPO

DPO

ORPO

PDF RAG

CSV RAG

Requirements

PYTHON

NLP

MACHINE LEARNING BASICS

Description

Generative AI: From Fundamentals to Advanced ApplicationsThis comprehensive course is designed to equip learners with a deep understanding of Generative AI, particularly focusing on Large Language Models (LLMs) and their applications. You will delve into the core concepts, practical implementation techniques, and ethical considerations surrounding this transformative technology.What You Will Learn:Foundational Knowledge: Grasp the evolution of AI, understand the core principles of Generative AI, and explore its diverse use cases.LLM Architecture and Training: Gain insights into the architecture of LLMs, their training processes, and the factors influencing their performance.Prompt Engineering: Master the art of crafting effective prompts to maximize LLM capabilities and overcome limitations.Fine-Tuning and Optimization: Learn how to tailor LLMs to specific tasks through fine-tuning and explore techniques like PEFT and RLHF.RAG and Real-World Applications: Discover how to integrate LLMs with external knowledge sources using Retrieval Augmented Generation (RAG) and explore practical applications.Ethical Considerations: Understand the ethical implications of Generative AI and responsible AI practices.By the end of this course, you will be equipped to build and deploy robust Generative AI solutions, addressing real-world challenges while adhering to ethical guidelines. Whether you are a data scientist, developer, or business professional, this course will provide you with the necessary skills to thrive in the era of Generative AI.Course Structure:The course is structured into 12 sections, covering a wide range of topics from foundational concepts to advanced techniques. Each section includes multiple lectures, providing a comprehensive learning experience.Section 1: Introduction to Generative AISection 2: LLM Architecture and ResourcesSection 3: Generative AI LLM LifecycleSection 4: Prompt Engineering SetupSection 5: LLM PropertiesSection 6: Prompt Engineering Basic GuidelinesSection 7: Better Prompting TechniquesSection 8: Full Fine TuningSection 9: PEFT - LORASection 10: RLHFSection 11: RAGSection 12: Generative AI for Vision (Preview)

Overview

Section 1: Introduction

Lecture 1 What is Generative AI

Lecture 2 What was before GENAI

Lecture 3 GEN AI TOOLS

Lecture 4 Better use of GEN AI

Lecture 5 GENAI USE CASE WRITING

Lecture 6 GEN AI Reading use cases

Lecture 7 gen AI Usecase chatting

Lecture 8 How to get Better Results from LLM

Lecture 9 Responsible AI

Section 2: LLM Shape size Resources needs

Lecture 10 Augmentation vs Automation

Lecture 11 The Kalpan Paper

Lecture 12 The Chinchilla Paper

Lecture 13 Transformers

Section 3: Generative AI LLM lifecycle

Lecture 14 GEN AI LIFE CYCLE

Lecture 15 RAG INTRO

Lecture 16 Fine tuning model intuition

Lecture 17 RLHF INTUTION

Lecture 18 Tools & Agents

Section 4: Prompt Engineering - set up and Prompt template

Lecture 19 Prompt Engineering - Introduction

Lecture 20 LLM configuration parameters

Lecture 21 Lecture 2: Llama 2 vs Llama 2 chat

Lecture 22 Set up using Lamma 2

Section 5: LLM Properties

Lecture 23 Stateless LLMs

Lecture 24 Base LLM VS Fine Tuned LLM

Lecture 25 System Prompts

Lecture 26 Quantized models

Lecture 27 Quantized Models Notebook

Lecture 28 AWQ SETUP and usage of notebook

Section 6: Prompt Engineering Basic Guidelines

Lecture 29 Check Conditions & assumptions

Lecture 30 Clear Instructions & Delimiters

Lecture 31 Specific Output Structure

Lecture 32 Few Shot Prompting

Lecture 33 Give time to think

Lecture 34 Hallucination

Section 7: Better Prompting Techniques

Lecture 35 Iterative Prompting

Lecture 36 Issues While summarizing

Lecture 37 summarize

Lecture 38 Inference

Lecture 39 Transformation

Lecture 40 Expanding

Lecture 41 Prompt Tuning

Section 8: Full Fine Tuning

Lecture 42 LLM FINE TUNING

Lecture 43 GLUE SUPER GLUE

Lecture 44 HELM

Lecture 45 LLM FINE TUNING Implementation

Section 9: PEFT - LORA

Lecture 46 PEFT

Lecture 47 QLORA

Lecture 48 PEFT Implementation

Section 10: RLHF

Lecture 49 PPO

Lecture 50 DPO VS ORPO

Section 11: RAG

Lecture 51 Using Langchain with Ollama to perform RAG with PDFs

Lecture 52 RAG With CSV File

Section 12: GEN AI for Vision - up next

Lecture 53 Image prompt engineering

Lecture 54 Stable Diffusion

Lecture 55 Stable diffusion model train methods

Lecture 56 Stable Diffusion Resources

Lecture 57 FORGE setup

DATA SCIENTISTS,ML Practitioners