{"id":4188,"date":"2024-02-08T09:37:37","date_gmt":"2024-02-08T09:37:37","guid":{"rendered":"https:\/\/cloudxlab.com\/blog\/?p=4188"},"modified":"2025-11-11T20:00:51","modified_gmt":"2025-11-11T20:00:51","slug":"building-your-own-chatgpt-from-scratch","status":"publish","type":"post","link":"https:\/\/cloudxlab.com\/blog\/building-your-own-chatgpt-from-scratch\/","title":{"rendered":"How to build\/code ChatGPT from scratch?"},"content":{"rendered":"\n

In a world where technology constantly pushes the boundaries of human imagination, one phenomenon stands out: ChatGPT<\/strong>. You’ve probably experienced its magic, admired how it can chat meaningfully, and maybe even wondered how it all works inside. ChatGPT is more than just a program; it’s a gateway to the realms of artificial intelligence, showcasing the amazing progress we’ve made in machine learning.<\/p>\n\n\n\n

At its core, ChatGPT is built on a technology called Generative Pre-trained Transformer<\/strong> (GPT). But what does that really mean? Let’s understand in this blog.<\/p>\n\n\n\n

In this blog, we’ll explore the fundamentals of machine learning, including how machines generate words. We’ll delve into the transformer architecture<\/strong> and its attention<\/strong> mechanisms. Then, we’ll demystify GPT and its role in AI. Finally, we’ll embark on coding our own GPT from scratch, bridging theory and practice in artificial intelligence.<\/p>\n\n\n\n

How does Machine learn?<\/h2>\n\n\n\n

Imagine a network of interconnected knobs\u2014this is a neural network<\/strong>, inspired by our own brains. In this network, information flows through nodes, just like thoughts in our minds. Each node processes information and passes it along to the next, making decisions as it goes.<\/p>\n\n\n\n

Each knob represents a neuron<\/strong>, a fundamental unit of processing. As information flows through this network, these neurons spring to action, analyzing, interpreting, and transmitting data. It’s similar to how thoughts travel through your mind\u2014constantly interacting and influencing one another to form a coherent understanding of the world around you. In a neural network, these interactions pave the way for learning, adaptation, and intelligent decision-making, mirroring the complex dynamics of the human mind in the digital realm.<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n\n\n\n\n

During the training phase of a neural network, we essentially guide it to understand patterns in data. We start by showing the network examples: we say, “Here’s the input, and here’s what we expect the output to be.” Then comes the fascinating part: we adjust\/tweak these knobs<\/strong>, so that the network gets better at predicting the correct output for a given input.<\/p>\n\n\n\n

As we tweak these knobs, our goal is simple: we want the network to get closer and closer to producing outputs that match our expectations. It’s like fine-tuning an instrument to play the perfect melody. Gradually, through this process, the network starts giving outputs that align more closely with what we anticipate. This adjustment process, known as backpropagation<\/strong>, involves fine-tuning the connections to align the network’s predictions with the provided input-output pairs. For understanding backpropagation better, you can refer to the following blog.<\/p>\n\n\n\n

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Coding Backpropagation and Gradient Descent From Scratch without using any libraries<\/a><\/blockquote>