Attention Is All You Need: In-Depth Walkthrough

In this blog post, I will walk through the “Attention Is All You Need,” explaining the mechanisms of the Transformer architecture that made it state-of-the-art. The goal is to understand why the Transformer was so groundbreaking and how it achieves these capabilities by implementing each part manually, relying only on NumPy without any out-of-the-box packages from Keras or PyTorch. Being a deep-learning beginner myself, I will try to add as much depth as possible to explain the author’s design choices since the original paper assumed a lot of implicit background knowledge that may not have always been apparent to novices. Hence, this post is best suited for beginners who have a basic understanding of machine and deep learning concepts and general linear algebra (vectors and matrix multiplication). The post will be split up into three parts: Why the Transformer architecture was so groundbreaking First, I will introduce some background information on the state of sequence modeling at the

In this blog post, I will walk through the “Attention Is All You Need,” explaining the mechanisms of the Transformer architecture that made it state-of-the-art. The goal is to understand why the Transformer was so groundbreaking and how it achieves these capabilities by implementing each part manually, relying only on NumPy without any out-of-the-box packages from Keras or PyTorch. Being a deep-learning beginner myself, I will try to add as much depth as possible to explain the author’s design choices since the original paper assumed a lot of implicit background knowledge that may not have alw