[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:
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Foundational Knowledge: Grasp the evolution of AI, understand the core principles of Generative AI, and explore its diverse use cases.
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LLM Architecture and Training: Gain insights into the architecture of LLMs, their training processes, and the factors influencing their performance.
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Prompt Engineering: Master the art of crafting effective prompts to maximize LLM capabilities and overcome limitations.
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Fine-Tuning and Optimization: Learn how to tailor LLMs to specific tasks through fine-tuning and explore techniques like PEFT and RLHF.
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RAG and Real-World Applications: Discover how to integrate LLMs with external knowledge sources using Retrieval Augmented Generation (RAG) and explore practical applications.
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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.
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Section 1: Introduction to Generative AI
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Section 2: LLM Architecture and Resources
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Section 3: Generative AI LLM Lifecycle
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Section 4: Prompt Engineering Setup
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Section 5: LLM Properties
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Section 6: Prompt Engineering Basic Guidelines
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Section 7: Better Prompting Techniques
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Section 8: Full Fine Tuning
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Section 9: PEFT – LORA
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Section 10: RLHF
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Section 11: RAG
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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
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MG Analytics
Data Scientist and Professional Trainer
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