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A Deep Dive Into the Inner Workings of Large Language Models: 🧠 Transformer Architecture Explained: The Brain Behind LLMs

December 6, 2022 - By Kinshuk Dutta

🔍 Introduction: Beyond Thought Simulation In our previous blog on Thought Generation in AI and NLP, we explored how modern AI systems can simulate reasoning, explanation, and creativity. At the heart of this capability lies a game-changing innovation in deep learning: the Transformer architecture. Originally introduced in the groundbreaking paper Attention is All You Need by Vaswani et al. in 2017, transformers have become the standard building block for nearly every large language model (LLM)—including GPT, BERT, PaLM, and Claude. This blog takes a hardcore technical deep dive into the full transformer architecture diagram you see above. Whether you’re a…

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Large Language Models - Natural Language Processing (NLP)

Tracing the Evolution from Neural Networks to Transformers and the Rise of LLMs in Modern NLP: 🧠 From Syntax to Semantics: How Neural Networks Empower NLP and Large Language Models

October 15, 2021 - By Kinshuk Dutta

In 2019, we explored the foundations of neural networks—how layers of interconnected nodes mimic the human brain to extract patterns from data. Since then, one area where neural networks have truly transformed the landscape is Natural Language Processing (NLP). What was once rule-based and statistical has now evolved into something more fluid, contextual, and surprisingly human-like—thanks to Large Language Models (LLMs) built atop deep neural architectures. We touched upon this topic in early 2020 in our blog 🧠 Understanding the Correlation Between NLP and LLMs lets keep momentum and try understand Neural Networks empowers NLP and LLM. The NLP Challenge:…

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