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Warezcrackfull.com » Tutorial » [NEW] (GEN AI) Master LLM Prompt Engineering– All You Need

[NEW] (GEN AI) Master LLM Prompt Engineering– All You Need

Author: warezcrackfull on 23-04-2024, 22:36, Views: 0

[NEW] (GEN AI) Master LLM Prompt Engineering– All You Need
Free Download [NEW] (GEN AI) Master LLM Prompt Engineering– All You Need
Published 4/2024
Created by MG Analytics
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 21 Lectures ( 4h 50m ) | Size: 2.2 GB


Role Prompting, Few-Shot Prompting, Hallucination, Iterative Prompting, Structure, Summarization, Inference, Expanding
What you'll learn:
Artificial Intelligence
Deep Learning
Data Science
Generative AI
LLM
Large Language Model
Prompt Engineering
llama
GPTQ
GGUF
AWQ
zero shot Learning
One shot Learning
Guidelines
Inference
Expanding
Summarizing
Prompt creation
Requirements:
Python
Eagerness to learn and explore Prompt Engineering
Beginners who what to learn about usage of LLMs
Description:
Welcome to the Captivating World of LLM Prompt Engineering!This course empowers you to unlock the true potential of Large Language Models (LLMs), regardless of your experience level. Whether you're a seasoned professional or a curious beginner, this comprehensive program equips you with the skills to become a master of LLM prompt engineering.Master the Art of Crafting Powerful Prompts:Diverse Task Applications: Craft effective prompts tailored to various tasks, including generating informative summaries, creating captivating stories, or even translating languages, all through the power of well-designed prompts.Advanced Techniques Exploration: Move beyond the basics and delve into advanced concepts like iterative prompting, where you refine your prompt based on the LLM's initial output. Additionally, explore few-shot learning, allowing you to achieve impressive results even with limited data.Core LLM Concepts Demystified: Gain a solid understanding of fundamental LLM properties like statelessness and quantization. Explore how these properties impact prompt design and LLM behavior. Learn to identify and mitigate potential hallucinations in LLM outputs.Unleash LLM Capabilities Through Hands-on Learning:Code Walkthroughs Deepen Understanding: Go beyond theory with interactive code walkthroughs using Lamma 2 as a platform. Actively explore code examples to gain practical experience in setting up, configuring LLMs, working with advanced models (e.g., quantized models), and leveraging specialized notebooks like AWQ for optimized workflows.Real-World Applications Solidify Skills: This course emphasizes the practical application of LLM prompt engineering. Learn how to tailor prompts to solve specific real-world problems, ensuring accurate and creative AI outputs. Translate your newfound knowledge into tangible results.Structured Learning Journey for Success:Clear and Concise Explanations: Simplify complex topics with bite-sized lessons and clear explanations.Interactive Learning Approach: Utilize a variety of learning methods, including interactive code walkthroughs, to reinforce understanding and foster your development as an LLM prompt engineering expert.Progressive Curriculum Design: Build your expertise step-by-step, starting with the fundamentals of LLMs and prompt engineering and progressing to advanced techniques.Embrace the Future of AI Interaction:By mastering LLM prompt engineering, you'll be at the forefront of the human-AI interaction revolution. This course equips you with the skills and knowledge to confidently navigate this exciting field and unlock the true power of LLMs. Let's embark on this journey together and explore the boundless possibilities of AI!
Who this course is for:
Anyone who wants to learn about LLM prompt Engineering
Beginner
Data Scientist
Beginner ML practitioners eager to learn Deep Learning
Python Developers with basic ML knowledge
Anyone who wants to learn about deep learning
Homepage
https://www.udemy.com/course/gen-ai-master-llm-prompt-engineering/







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  • Tags: NEW, GEN, Master, LLM, Prompt
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