Simply explained

Simply explained

5/8/2023

link

https://confusedbit.dev/posts/how_does_gpt_work/

summary

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.

tags

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