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What are Transformers in Machine Learning?
What are Transformers in Machine Learning? - Flow Card Image

Imagine you are trying to understand a long, complicated book, and you have a friend who can remember everything they've read so far and can quickly find the important parts when needed. This friend helps you understand the book by focusing on the most important parts and connecting them together.

In machine learning, a "transformer" is like that helpful friend. It's a type of model that looks at all the words (or pieces of information) in a sentence, a paragraph, or even a whole book and figures out which parts are important and how they relate to each other. This helps the model understand and generate human language more effectively.

Before transformers, computers struggled to understand long pieces of text because they could only focus on a few words at a time. Transformers changed this by allowing models to consider the entire context at once, making them much better at tasks like translating languages, answering questions, and even writing stories.

Key Concepts:
1. Attention: Transformers pay attention to all the words and decide which ones are the most important.
2. Context: They look at the context of each word, meaning they understand words based on the surrounding words.
3. Learning: They learn from lots of text data, improving their ability to understand and generate language over time.

A simple illustrated guide to understanding The Transformer: https://shorturl.at/30XCB
A more in-depth reading: https://rb.gy/avnr0z
Illustrated video explanation: https://rb.gy/a42vyl
Stanford lecture on transformers: https://rb.gy/582t35

Categories : Machine Learning

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