Mastering Multimodal RAG
A course focused on mastering multimodal Retrieval Augmented Generation (RAG) and embeddings, which are fundamental components often stored and managed by vector databases.
About this tool
Mastering Multimodal RAG & Embeddings with Amazon Nova & Bedrock
Level: Beginner Duration: 5 Hours Students Enrolled: 1500+ Average Rating: 4.8
About this Course
This course provides a strong foundation in natural language processing by delving into:
- Word embeddings, tokenization, Byte Pair Encoding (BPE), and data sampling techniques.
- Utilization of Amazon's Titan Text Embeddings for effective text representation, enhancing AI application performance.
- Integration of various data modalities using Amazon Nova and Bedrock for developing advanced, AI-powered solutions.
Learning Outcomes
Upon completion, you will be able to:
- Understand Embeddings: Learn how embeddings enhance NLP and LLM capabilities.
- Explore Multimodal RAG: Master retrieval-augmented generation with multimodal data.
- Utilize Amazon Nova & Bedrock: Leverage Amazon Nova and Bedrock for AI-powered solutions.
Course Curriculum
The comprehensive curriculum covers:
1. Embedding in NLP and LLMs
- Introduction to the course
- Understanding word Embeddings and Tokenization
- Implementing Byte-Pair Encoding (BPE)
- Data sampling with a sliding window
2. Amazon Bedrock & Amazon Titan Text Embeddings model
- Exploring Embedding model on Amazon Bedrock
3. Multimodal LLMs
- Multimodals and Transformers for vision
- Understanding CLIP
- Text Generation Multimodals
4. Multimodal RAG
- What is RAG
- What is Multimodal RAG
- Building Multimodal RAG with Amazon Bedrock, Amazon Nova and LangChain
Instructor
- Suman Debnath: Principal Developer Advocate for Machine Learning at AWS.
Pricing
- Enrollment: Free
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