Hello everyone, welcome back to our channel. Today, we're going to be diving into the world of natural language processing and discussing the revolutionary algorithm that's taking the NLP world by storm, GPT or generative pre-trained transformer. In this video, we'll be covering all aspects of the GPT algorithm from its conceptual framework to its operational architecture so you can understand its workings in depth. Conceptual overview. To begin with, let's explore the fundamental concepts that underpin the GPT algorithm. GPT is a deep learning algorithm that is specifically designed for NLP applications. At its core, the algorithm is based on a neural network architecture that is known as the transformer. The transformer was introduced by Veswani at all. In 2017, it has since become one of the most popular architectures in the field of NLP. The transformer is a self-attention based architecture that is designed to handle sequential data-like text. The key advantage of the transformer is its ability to capture the context and long-range dependencies of the text effectively. This makes it ideal for applications like language translation, sentiment analysis, and text generation. Now, GPT takes the transformer architecture and trains it on massive amounts of text data to pre-traine the model. This pre-training process enables the algorithm to learn the patterns and structures of language effectively. The result is a language model that can generate high-quality text.