Transformers Unveiled: The Magic Behind Language Processing Simplified
The Transformer model is a neural network that learns context and understanding through sequential data analysis. It uses a modern and evolving mathematical technique set, generally known as attention or self-attention. This set helps identify how distant data elements influence and depend on one another.
In this article, we’ll demystify the enchanting world of Transformers, breaking down the complexity into simple, everyday analogies that will leave you marvelling at their linguistic prowess.
The Transformer Architecture: Breaking It Down
Certainly! Let’s break down the layers of a Transformer model in the simplest terms:
1. Input Layer — The Messenger:
— Imagine your sentence as a message. The input layer receives this message and prepares it for processing.
2. Self-Attention Layer — The Listener:
— Think of this layer as a listener at a party. It pays attention to each word in the sentence, focusing more on important words just like you might pay more attention to interesting conversations.
3. Multi-Head Attention — Team of Listeners:
— Instead of one listener, imagine a team of friends listening. They each focus…
