Implementing Retrival with Modal data
2025-01-22
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 27.87 GB | Duration: 33h 30m
2025-01-22
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 27.87 GB | Duration: 33h 30m
Learn how to implement Retrival with multimodal data and modal system and search and more!
What you'll learn
You are going to Implement Retrieval-Augmented Generation with multimodal dat
You are going to learn implement multimodal search
You are going to learn how to retrieve the data from database
You are going to learn retrieval and generation components
Requirements
You need to have internet to take this course
Description
Retrieval Generation is a hybrid model combining retrieval-based and generative approaches to provide more accurate and contextually rich answers. In Retrieval-Augmented Generation, a language model is augmented by retrieving relevant information from a knowledge base or document corpus before generating responses. This significantly improves accuracy, especially for specialized or factual queries, as it combines the generative flexibility of the language model with retrieval systems’ precision.Implementing Retrieval-Augmented Generation with multimodal data brings an additional layer of complexity and richness. Multimodal data refers to information that comes in various forms, such as text, images, audio, or video. With the rise of sophisticated natural language processing and computer vision models, the integration of text with visual or audio data is becoming increasingly feasible. In a RAG framework, the retrieval component could access multimodal databases, fetching not only textual documents but also images or videos, while the generative component synthesizes information from these diverse data sources to provide a coherent, context-aware response.implementing Implementing Retrieval-Augmented with multimodal data creates systems that are richer and more contextually relevant than purely text-based models. By accessing and synthesizing information from diverse data sources, these systems provide highly informative responses that are particularly suited for applications requiring a deep understanding of visual, auditory, and textual information combined.
Who this course is for:
If you want to learn with detailed examples for every concept, this course will be for you