New 2024: The Generative Ai Lifecycle: A Primer
[New] 2024: The Generative Ai Lifecycle: A Primer
Published 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.22 GB | Duration: 3h 4m
A Primer, Prompt Engineering, RAG, PEFT, FINE TUNING, Evaluation Metrics and Benchmarks
What you'll learn
GEN AI lifecycle
Model Selection
Prompt Engineering
Retrieval Augmented Generation (RAG) IN LLMs
FINE TUNING of LLM Model
Tools Agents
Evaluation Metrics
Requirements
Interest in Generative AI
No programming experience needed
Description
IntroductionGenerative AI has rapidly emerged as a transformative force, revolutionizing industries from content creation to drug discovery. At the heart of this revolution lie Large Language Models (LLMs), which have the potential to revolutionize how we interact with information and generate new content.This course serves as a foundational introduction to the generative AI lifecycle, providing you with a comprehensive overview of the key stages involved in developing and deploying LLMs. By understanding the entire process, you'll gain valuable insights into the challenges, opportunities, and best practices associated with generative AI.Course ObjectivesGain a foundational understanding of the key stages in the generative AI lifecycle.Explore the role of LLMs in driving innovation and problem-solving.Learn about the importance of data quality and preprocessing in LLM development.Understand the different techniques used to train and fine-tune LLMs.Explore the role of evaluation metrics in assessing LLM performance.Discover the potential applications of LLMs across various domains.Course StructureThis course is designed to provide a concise overview of the generative AI lifecycle. Each lecture will introduce a key stage, providing you with essential information and context. For a more in-depth exploration of each topic, we recommend our comprehensive course, "Mastering Generative AI: From LLMs to Applications."Key Topics CoveredIntroduction to Generative AI and LLMsThe Generative AI LifecycleModel Selection for Pre-trained modelsModel Training and Fine-TuningEvaluation Metrics and BenchmarkingApplications of Generative AIBy completing this course, you'll have a solid foundation in the generative AI lifecycle, enabling you to make informed decisions and effectively leverage LLMs in your work. We encourage you to explore our more advanced course, "Mastering Generative AI: From LLMs to Applications," for a deeper dive into each topic and practical hands-on experience.
Overview
Section 1: Introduction
Lecture 1 Introduction to Gen-AI
Lecture 2 GEN-AI Lifecycle
Lecture 3 How to improve LLM responses
Lecture 4 Prompt Engineering
Lecture 5 Introduction to RAG -Retrieval Augmented Generation IN LLMs
Lecture 6 Introduction to Prompt tuning
Lecture 7 Quantization intuition challenges and need
Lecture 8 Fine Tuning Model Intuition
Lecture 9 Introduction to LLM Fine Tuning
Lecture 10 Evaluation Metrics Rouge Score
Lecture 11 BLEU Score
Lecture 12 Introduction to RLHF
Lecture 13 Evaluation Benchmarks : GLUE SUPER GLUE
Lecture 14 Evaluation Benchmarks : HELM
Lecture 15 Tools and Agents
Lecture 16 Loading your LLM using Langchain
Tech managers,directors,ML Engineers,other tech leaders,Software Engineers,AI Developers,Data Scientists
What you'll learn
GEN AI lifecycle
Model Selection
Prompt Engineering
Retrieval Augmented Generation (RAG) IN LLMs
FINE TUNING of LLM Model
Tools Agents
Evaluation Metrics
Requirements
Interest in Generative AI
No programming experience needed
Description
IntroductionGenerative AI has rapidly emerged as a transformative force, revolutionizing industries from content creation to drug discovery. At the heart of this revolution lie Large Language Models (LLMs), which have the potential to revolutionize how we interact with information and generate new content.This course serves as a foundational introduction to the generative AI lifecycle, providing you with a comprehensive overview of the key stages involved in developing and deploying LLMs. By understanding the entire process, you'll gain valuable insights into the challenges, opportunities, and best practices associated with generative AI.Course ObjectivesGain a foundational understanding of the key stages in the generative AI lifecycle.Explore the role of LLMs in driving innovation and problem-solving.Learn about the importance of data quality and preprocessing in LLM development.Understand the different techniques used to train and fine-tune LLMs.Explore the role of evaluation metrics in assessing LLM performance.Discover the potential applications of LLMs across various domains.Course StructureThis course is designed to provide a concise overview of the generative AI lifecycle. Each lecture will introduce a key stage, providing you with essential information and context. For a more in-depth exploration of each topic, we recommend our comprehensive course, "Mastering Generative AI: From LLMs to Applications."Key Topics CoveredIntroduction to Generative AI and LLMsThe Generative AI LifecycleModel Selection for Pre-trained modelsModel Training and Fine-TuningEvaluation Metrics and BenchmarkingApplications of Generative AIBy completing this course, you'll have a solid foundation in the generative AI lifecycle, enabling you to make informed decisions and effectively leverage LLMs in your work. We encourage you to explore our more advanced course, "Mastering Generative AI: From LLMs to Applications," for a deeper dive into each topic and practical hands-on experience.
Overview
Section 1: Introduction
Lecture 1 Introduction to Gen-AI
Lecture 2 GEN-AI Lifecycle
Lecture 3 How to improve LLM responses
Lecture 4 Prompt Engineering
Lecture 5 Introduction to RAG -Retrieval Augmented Generation IN LLMs
Lecture 6 Introduction to Prompt tuning
Lecture 7 Quantization intuition challenges and need
Lecture 8 Fine Tuning Model Intuition
Lecture 9 Introduction to LLM Fine Tuning
Lecture 10 Evaluation Metrics Rouge Score
Lecture 11 BLEU Score
Lecture 12 Introduction to RLHF
Lecture 13 Evaluation Benchmarks : GLUE SUPER GLUE
Lecture 14 Evaluation Benchmarks : HELM
Lecture 15 Tools and Agents
Lecture 16 Loading your LLM using Langchain
Tech managers,directors,ML Engineers,other tech leaders,Software Engineers,AI Developers,Data Scientists
