Torrent details for "Retrieval Augmented Generation - RAG Fine Tuning Explained --> [ DevCourseWeb ]" Log in to bookmark
Controls:
Language:
EnglishTotal Size:
386.56 MB
Info Hash:
e8f9351418c8753bda7c89e229946b9e62a5cd29
Added By:
Added:
13-01-2025 15:36
Views:
72
Health:
Seeds:
2
Leechers:
6
Completed:
7
[ DevCourseWeb.com ] Retrieval Augmented Generation - RAG Fine Tuning Explained
If You Need More Stuff, kindly Visit and Support Us -->> https://DevCourseWeb.com
Published 12/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 32m | Size: 387 MB
Learn Retrieval Augmented Generation (RAG) Fine-Tuning and LLM Optimization to Build Accurate Real-World AI Applications
What you'll learn
Understand the fundamentals of Retrieval Augmented Generation (RAG) and how it enhances the performance of Large Language Models (LLMs).
Learn how to fine-tune LLMs to align with domain-specific tasks and improve accuracy, relevance, and reliability.
Gain hands-on knowledge of how to implement RAG workflows to connect LLMs with real-time, grounded data sources.
Explore real-world scenarios and use cases where RAG and fine-tuning empower AI to deliver precise, actionable results in enterprise environments.
Develop the skills to create custom datasets for fine-tuning and train AI models to adapt to specific organizational needs.
Master techniques to reduce AI hallucination and ensure AI-generated responses are grounded in facts and context.
Understand how to combine RAG with fine-tuning (RAFT) to create cutting-edge, domain-specific AI solutions.
Discover the inner workings of LLMs – Understand how large language models generate responses using probabilistic methods and why this can lead to hallucination
Learn the importance of context in AI interactions – Explore how providing detailed prompts and context enhances LLM accuracy and relevance.
Understand embeddings and vector databases – Gain insights into how embeddings help AI interpret queries and retrieve relevant information efficiently.
Explore knowledge graphs – See how knowledge graphs reduce ambiguity, enhancing AI’s ability to understand relationships between concepts for more accurate resp
Implement RAFT (Retrieval-Augmented Fine-Tuning) – Master the combination of RAG and fine-tuning to develop AI systems that can retrieve data and respond accura
Recognize enterprise use cases for RAG and fine-tuning – Learn how companies use RAG to power AI chatbots, virtual assistants, and customer service tools that a
Design AI solutions that scale – Understand how to implement RAG systems across large organizations, ensuring AI assistants remain up-to-date with evolving data
Requirements
Basic understanding of AI and machine learning concepts – Familiarity with how AI models work will help, but is not required.
Interest in Large Language Models (LLMs) – A curiosity about how models like GPT function and can be improved.
No advanced programming experience required – This course focuses on concepts, workflows, and real-world applications. Technical details are explained in an accessible way.
Optional: Familiarity with Python or AI frameworks can enhance your learning experience, but the course covers essential topics without heavy coding.
If You Need More Stuff, kindly Visit and Support Us -->> https://FreeCourseWeb.com
Get More Tutorials and Support Us -->> https://DevCourseWeb.com
We upload these learning materials for the people from all over the world, who have the talent and motivation to sharpen their skills/ knowledge but do not have the financial support to afford the materials. If you like this content and if you are truly in a position that you can actually buy the materials, then Please, we repeat, Please, Support Authors. They Deserve it! Because always remember, without "Them", you and we won't be here having this conversation. Think about it! Peace...
If You Need More Stuff, kindly Visit and Support Us -->> https://DevCourseWeb.com
Published 12/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 32m | Size: 387 MB
Learn Retrieval Augmented Generation (RAG) Fine-Tuning and LLM Optimization to Build Accurate Real-World AI Applications
What you'll learn
Understand the fundamentals of Retrieval Augmented Generation (RAG) and how it enhances the performance of Large Language Models (LLMs).
Learn how to fine-tune LLMs to align with domain-specific tasks and improve accuracy, relevance, and reliability.
Gain hands-on knowledge of how to implement RAG workflows to connect LLMs with real-time, grounded data sources.
Explore real-world scenarios and use cases where RAG and fine-tuning empower AI to deliver precise, actionable results in enterprise environments.
Develop the skills to create custom datasets for fine-tuning and train AI models to adapt to specific organizational needs.
Master techniques to reduce AI hallucination and ensure AI-generated responses are grounded in facts and context.
Understand how to combine RAG with fine-tuning (RAFT) to create cutting-edge, domain-specific AI solutions.
Discover the inner workings of LLMs – Understand how large language models generate responses using probabilistic methods and why this can lead to hallucination
Learn the importance of context in AI interactions – Explore how providing detailed prompts and context enhances LLM accuracy and relevance.
Understand embeddings and vector databases – Gain insights into how embeddings help AI interpret queries and retrieve relevant information efficiently.
Explore knowledge graphs – See how knowledge graphs reduce ambiguity, enhancing AI’s ability to understand relationships between concepts for more accurate resp
Implement RAFT (Retrieval-Augmented Fine-Tuning) – Master the combination of RAG and fine-tuning to develop AI systems that can retrieve data and respond accura
Recognize enterprise use cases for RAG and fine-tuning – Learn how companies use RAG to power AI chatbots, virtual assistants, and customer service tools that a
Design AI solutions that scale – Understand how to implement RAG systems across large organizations, ensuring AI assistants remain up-to-date with evolving data
Requirements
Basic understanding of AI and machine learning concepts – Familiarity with how AI models work will help, but is not required.
Interest in Large Language Models (LLMs) – A curiosity about how models like GPT function and can be improved.
No advanced programming experience required – This course focuses on concepts, workflows, and real-world applications. Technical details are explained in an accessible way.
Optional: Familiarity with Python or AI frameworks can enhance your learning experience, but the course covers essential topics without heavy coding.
Get More Tutorials and Support Us -->> https://DevCourseWeb.com
We upload these learning materials for the people from all over the world, who have the talent and motivation to sharpen their skills/ knowledge but do not have the financial support to afford the materials. If you like this content and if you are truly in a position that you can actually buy the materials, then Please, we repeat, Please, Support Authors. They Deserve it! Because always remember, without "Them", you and we won't be here having this conversation. Think about it! Peace...
