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Vector Space Models and Embeddings in RAGs
Discover the power of Retrieval-Augmented Generation (RAG) in modern NLP applications. This course will teach you how to implement a RAG-based chatbot using Python and TensorFlow, focusing on text embeddings and retrieval techniques.
In the ever-evolving field of natural language processing,
integrating robust retrieval mechanisms with generation
models is crucial for creating advanced AI systems. In this
course, Vector Space Models and Embeddings in RAGs, you'll learn to implement
effective RAG-based chatbots. First, you'll explore the
foundational concepts of Retrieval-Augmented Generation
and understand its significance in enhancing language
models. Next, you'll discover how to represent text data
using various embedding techniques, analyzing their
properties and limitations. Finally, you'll learn how to
implement these embeddings in a practical RAG system to retrieve relevant information efficiently. When you're
finished with this course, you'll have the skills and
knowledge of RAG needed to develop advanced AI chatbots
capable of sophisticated text retrieval and response
generation.
Homepage:
Download link
rapidgator.net:
nitroflare.com:
Published 6/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz
Language: English | Size: 74.19 MB | Duration: 15m 29s
MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz
Language: English | Size: 74.19 MB | Duration: 15m 29s
Discover the power of Retrieval-Augmented Generation (RAG) in modern NLP applications. This course will teach you how to implement a RAG-based chatbot using Python and TensorFlow, focusing on text embeddings and retrieval techniques.
In the ever-evolving field of natural language processing,
integrating robust retrieval mechanisms with generation
models is crucial for creating advanced AI systems. In this
course, Vector Space Models and Embeddings in RAGs, you'll learn to implement
effective RAG-based chatbots. First, you'll explore the
foundational concepts of Retrieval-Augmented Generation
and understand its significance in enhancing language
models. Next, you'll discover how to represent text data
using various embedding techniques, analyzing their
properties and limitations. Finally, you'll learn how to
implement these embeddings in a practical RAG system to retrieve relevant information efficiently. When you're
finished with this course, you'll have the skills and
knowledge of RAG needed to develop advanced AI chatbots
capable of sophisticated text retrieval and response
generation.
Homepage:
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Download link
rapidgator.net:
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nitroflare.com:
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