Simply explained

5/8/2023

This blog post delves into the workings of GPT (Generative Pre-trained Transformer), a state-of-the-art language model developed by OpenAI. It provides a detailed explanation of how GPT works, starting with the pre-training phase where the model learns from a large corpus of text data. The post discusses the architecture of GPT, highlighting the transformer model and the self-attention mechanism that enables it to generate coherent and contextually relevant text. It also covers the fine-tuning phase, where the model is trained on specific tasks to make it more useful for various applications. Overall, this post provides a comprehensive overview of GPT and its underlying mechanisms, offering insights into the impressive capabilities of this language model.

data preprocessing ꞏ large-scale models ꞏ text generation techniques ꞏ training process ꞏ contextualized embeddings ꞏ text generation ꞏ language model ꞏ natural language processing ꞏ deep learning ꞏ contextual embeddings ꞏ attention mechanism ꞏ word embeddings ꞏ decoding process ꞏ neural networks ꞏ nlp research ꞏ big data ꞏ self-supervised learning ꞏ unsupervised learning ꞏ finetuning ꞏ language modeling ꞏ tokenization ꞏ computational linguistics ꞏ artificial intelligence ꞏ machine learning ꞏ input representation ꞏ language understanding ꞏ gpt ꞏ transformer model ꞏ deep neural networks ꞏ ai research ꞏ pretraining ꞏ generative models ꞏ model architecture ꞏ transfer learning