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AI generations
Unit 1
Langchain
Introduction to Langchain
Applications of Langchain in AI Generations
Langchain and Natural Language Processing
Langchain and Neural Networks
Unit 2
Retrieval Augmented Generations
Introduction to Retrieval Augmented Generations
Retrieval Techniques in AI Generation
Applications of Retrieval Augmented Generations
Unit 3
Vector Embeddings
Introduction to Vector Embeddings
Word Embeddings
Image Embeddings
Graph Embeddings
Unit 3 • Chapter 1
Introduction to Vector Embeddings
Summary
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Concept Check
What is a key concept in Vector Embeddings?
Key concept not explicitly mentioned
Dimensionality reduction
Classification techniques
Feature engineering
Why are Vector Embeddings important in NLP?
Important for capturing semantic relationships
To improve computational efficiency
To reduce data storage requirements
To simplify data preprocessing
In what field are Vector Embeddings commonly used?
Computer vision
Commonly used in Natural Language Processing
Financial forecasting
Medical imaging
What is the main goal of Vector Embeddings?
To represent words or phrases as numerical vectors
To create graphical visualizations
To optimize database queries
To generate random text samples
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Word Embeddings