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Machine Learning
Encoders and Decoders in Machine Learning: The Building Blocks of Modern AI
Introduction Encoders and decoders are fundamental components in machine learning, particularly in neural networks designed for tasks involving data transformation.…
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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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Gen AI
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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Gen AI
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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Gen AI
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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Gen AI
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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Gen AI
KV Cache in Transformer Models
Optimizing Inference for Autoregressive Decoding Introduction Large language models (LLMs) like GPT, PaLM, and LLaMA rely on transformer architectures to…
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Gen AI
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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