Vector Embeddings for RAG and LLM | Vector Embedding models | HuggingFace | Embedding Techniques
Unlock the power of vector embeddings in Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs) with this comprehensive guide! 🌟 In this detailed video, I cover everything you need to know about embeddings, including:
What Are Embeddings?: A clear explanation with practical examples of converting words to embeddings.
3D Visualization: Plotting embeddings in 3D space to illustrate similarities between entities.
Similarity Metrics: Solved examples using cosine similarity, Euclidean distance, Jaccard similarity, and more.
Types of Embeddings: An exploration of word, sentence, and document embeddings.
Embedding Techniques: From neural net-based and contextualized embeddings (like transformers, GPT, ELMo) to Word2Vec, FastText, and more.
Open Source Models: How to access and implement different embedding models from Hugging Face, complete with Python code examples.
Whether you're a beginner or looking to deepen your understanding, this video provides valuable insights and practical knowledge to enhance your AI projects.
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