Understanding Sentences at Once with the Transformer Model

The Transformer is a neural network model that processes entire sentences simultaneously, instead of word by word.

It is widely used in Natural Language Processing (NLP) and serves as the core architecture behind large language models such as GPT and BERT.

Traditional RNNs and LSTMs handle input one word at a time, following the sequence order.

While this approach is advantageous for understanding the flow of a sentence, it is slow and struggles with retaining earlier information in longer sentences.

The Transformer was introduced to overcome these limitations.

The Transformer model analyzes all words at once, directly computing relationships between them for a more accurate grasp of sentence meaning.

In the next lesson, we will explore in detail one of the key components of the Transformer: the Self-Attention Mechanism.