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 Transformer. Attention Is All You Need Figure 1. 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…

related reading

• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.jalammar.github.io
• transformer_attention.pdfarxiv.org
• Everything About Transformerskrupadave.com
• A Mathematical Framework for Transformer Circuitstransformer-circuits.pub
• Transformers from Scratche2eml.school
• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time.jalammar.github.io
• Attention is all you need: Discovering the Transformer paper | Towards Data Sciencetowardsdatascience.com
• Transformer (deep learning) - Wikipediaen.wikipedia.org
• The Annotated Transformernlp.seas.harvard.edu
• Transformers from scratch | peterbloem.nlpeterbloem.nl
• 1706.03762arxiv.org
• Transformer (deep learning) - Wikipediaen.wikipedia.org