[NEW] 2024:Mastering Generative AI-From LLMs to Applications

[NEW] 2024:Mastering Generative AI-From LLMs to Applications, available at $54.99, with 63 lectures, and has 40 subscribers.

You will learn about 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 GEN AI Lifecycle This course is ideal for individuals who are DATA SCIENTISTS or ML Practitioners It is particularly useful for DATA SCIENTISTS or ML Practitioners.

Enroll now: [NEW] 2024:Mastering Generative AI-From LLMs to Applications

Summary

Title: [NEW] 2024:Mastering Generative AI-From LLMs to Applications

Price: $54.99

Number of Lectures: 63

Number of Published Lectures: 60

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 60

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will 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
GEN AI Lifecycle

Who Should Attend

DATA SCIENTISTS
ML Practitioners

Target Audiences

DATA SCIENTISTS
ML Practitioners

Generative AI: From Fundamentals to Advanced Applications

This 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 AI
Section 2: LLM Architecture and Resources
Section 3: Generative AI LLM Lifecycle
Section 4: Prompt Engineering Setup
Section 5: LLM Properties
Section 6: Prompt Engineering Basic Guidelines
Section 7: Better Prompting Techniques
Section 8: Full Fine Tuning
Section 9: PEFT – LORA
Section 10: RLHF
Section 11: RAG
Section 12: Generative AI for Vision (Preview)

Course Curriculum

Chapter 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

Chapter 2: LLM Shape size Resources needs

Lecture 1: Augmentation vs Automation

Lecture 2: The Kalpan Paper

Lecture 3: The Chinchilla Paper

Lecture 4: Transformers

Chapter 3: Generative AI LLM lifecycle

Lecture 1: GEN AI LIFE CYCLE

Lecture 2: RAG INTRO

Lecture 3: Fine tuning model intuition

Lecture 4: RLHF INTUTION

Lecture 5: Tools & Agents

Chapter 4: Prompt Engineering – set up and Prompt template

Lecture 1: Prompt Engineering – Introduction

Lecture 2: LLM configuration parameters

Lecture 3: Lecture 2: Llama 2 vs Llama 2 chat

Lecture 4: Set up using Lamma 2

Chapter 5: LLM Properties

Lecture 1: Stateless LLMs

Lecture 2: Base LLM VS Fine Tuned LLM

Lecture 3: System Prompts

Lecture 4: Quantized models

Lecture 5: Quantized Models Notebook

Lecture 6: AWQ SETUP and usage of notebook

Chapter 6: Prompt Engineering Basic Guidelines

Lecture 1: Check Conditions & assumptions

Lecture 2: Clear Instructions & Delimiters

Lecture 3: Specific Output Structure

Lecture 4: Few Shot Prompting

Lecture 5: Give time to think

Lecture 6: Hallucination

Chapter 7: Better Prompting Techniques

Lecture 1: Iterative Prompting

Lecture 2: Issues While summarizing

Lecture 3: summarize

Lecture 4: Inference

Lecture 5: Transformation

Lecture 6: Expanding

Lecture 7: Prompt Tuning

Chapter 8: RAG

Lecture 1: Langchain to access different models

Lecture 2: Using Langchain with Ollama to perform RAG with PDFs

Lecture 3: RAG With CSV File

Chapter 9: Full Fine Tuning

Lecture 1: LLM FINE TUNING

Lecture 2: GLUE SUPER GLUE

Lecture 3: HELM

Lecture 4: LLM FINE TUNING Implementation

Chapter 10: PEFT – LORA

Lecture 1: PEFT

Lecture 2: QLORA

Lecture 3: PEFT Implementation

Chapter 11: RLHF

Lecture 1: PPO

Lecture 2: DPO VS ORPO

Lecture 3: UNSLOTH

Lecture 4: DPO with Unsloth

Chapter 12: GEN AI for Vision – up next

Lecture 1: Image prompt engineering

Lecture 2: Stable Diffusion

Lecture 3: Stable diffusion model train methods

Lecture 4: Stable Diffusion Resources

Lecture 5: FORGE setup

Instructors

MG Analytics
Data Scientist and Professional Trainer

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Frequently Asked Questions

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