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LLMs, Unboxed (Inside LLMs)

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Large Language Model (LLM) is a term you have probably heard frequently in recent years, especially in the field of AI. LLMs are often discussed alongside terms such as chatbots, AI, AGI, and even 'black boxes.' However, at their core, LLMs are neural networks trained to learn patterns in language. For a formal introduction, a Large Language Model (LLM) is a type of neural network trained to predict the next token based on the tokens that came before it. That is the core idea behind an LLM. Think of it this way: imagine you have read thousands of books. Someone gives you a paragraph and asks you to predict what comes next. Because you have been exposed to so much language, you have learned patterns, grammar, vocabulary, writing styles, facts, and relationships between concepts. You can use those patterns to make a reasonable prediction about what comes next. LLMs do something similar. They learn patterns from the data they are trained on. The major difference is that, unlike...
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Understanding Convolutional Neural Networks

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Prerequisites This article assumes you have: A basic understanding of deep learning, particularly feedforward neural networks. Familiarity with high-school level mathematics. Introduction If you've spent any time learning about machine learning, you've almost certainly come across Artificial Neural Networks (ANNs) . They're often described as being inspired by the human brain. While that comparison isn't entirely inaccurate, it's more helpful to think of neural networks as mathematical models rather than biological simulations. At their core, neural networks are systems designed to learn patterns from data. An ANN is made up of many interconnected computational units, commonly referred to as neurons , which are arranged into layers. Each neuron performs a mathematical operation on its inputs before passing the result to the next layer. Although each individual computation is relatively simple, the combined effect of thousands or even millions of these operations all...
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