Transformers: How Do They Transform Your Data?
In the rapidly evolving landscape of artificial intelligence and machine learning, one innovation stands out for its profound impact on how we process, understand, and generate data: Transformers. Transformers have revolutionized the field of natural language processing (NLP) and beyond, powering some of today's most advanced AI applications. But what exactly are Transformers, and how do they manage to transform data in such groundbreaking ways? This article demystifies the inner workings of Transformer models, focusing on the encoder architecture. We will start by going through the implementation of a Transformer encoder in Python, breaking down its main components. Then, we will visualize how Transformers process and adapt input data during training.
While this blog doesn't cover every architectural detail, it provides an implementation and an overall understanding of the transformative power of Transformers. For an in-depth explanation of Transformers, I suggest you look at the excellent Stanford CS224-n course.
I also recommend following the GitHub repository associated with this article for additional details.
