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BERT vs GPT: A Guide to Two Powerful Language Models
In the world of natural language processing (NLP), two powerful models have taken the spotlight: BERT and GPT. While both are based on deep learning and use transformers, they differ in their architecture, functionality, and use cases. In this blog, we will break down these two models from scratch, explaining their key concepts, differences, and applications in a simple and easy-to-understand way.
What is NLP and Why Do We Need Models Like BERT and GPT?
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and human language. It’s used to enable machines to understand, interpret, and generate human language. NLP applications include chatbots, language translators, speech recognition systems, and much more.
To understand NLP, let’s think about how we, as humans, understand language. When you read or hear a sentence, your brain processes the meaning based on the words, grammar, context, and your prior knowledge. For a machine to do something similar, it needs a sophisticated model trained on vast amounts of data. This is where models like BERT and GPT come in.
