Gen AI
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Understanding Words vs. Tokens in Natural Language Processing
In both human communication and artificial intelligence, the way we break down language into manageable units is fundamental. While humans…
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Positional Encoding: The Compass of Sequence Order in Transformers
Introduction In the realm of transformer models, where parallel processing reigns supreme, positional encoding acts as a critical navigator. Unlike…
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Different Types of Retrieval-Augmented Generation (RAG) in AI
Retrieval-Augmented Generation (RAG) has emerged as a powerful technique in artificial intelligence, blending the strengths of retrieval systems and generative…
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The Role of Tokenizers in Large Language Models (LLMs): A Comprehensive Guide
Tokenizers are the unsung heroes of Large Language Models (LLMs), serving as the critical first step in transforming raw text…
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Attention Mechanism in Large Language Models
The Engine of Contextual Understanding Introduction Large Language Models (LLMs) like GPT-4, BERT, and T5 have revolutionized artificial intelligence by…
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Retrieval-Augmented Generation (RAG)
Enhancing AI with Dynamic Knowledge Integration IntroductionRetrieval-Augmented Generation (RAG) represents a transformative approach in natural language processing (NLP), merging the…
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LLM Pruning: A Comprehensive Guide to Model Compression
Introduction Large Language Models (LLMs) like GPT-4, BERT, and LLaMA have revolutionized AI with their ability to understand and generate…
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AI Agents: Short-Term vs. Long-Term Memory
How Machines Remember to Think, Act, and Learn Introduction AI agents—from chatbots to self-driving cars—rely on memory systems to process…
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Sparsity in Large Language Models (LLMs)
Introduction Large Language Models (LLMs) like GPT, BERT, and T5 have revolutionized natural language processing (NLP) by achieving state-of-the-art performance…
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Sparse Embeddings vs. Dense Embeddings : Things you must know
Introduction In the fields of machine learning and natural language processing (NLP), embeddings are a fundamental concept used to represent data in…
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