Following this book to teach myself about the transformer architecture in depth.

Some excellent resources I've come across along the way:

• Illustrated Guide to Transformers Neural Network: A step by step explanation - by Michael Phi (@LearnedVector)
• Let's build GPT: from scratch, in code, spelled out. - by the legendary Andrej Karpathy (@karpathy)
• Transformers from Scratch - by Peter Bloem (@pbloem)
• Lil'Log > The Transformer Family Version 2.0 - by Lilian Weng (@lilianweng)
• The Illustrated Transformer - by Jay Alammar (@jalammar)
• Transformer Architecture: The Positional Encoding - by Amirhossein Kazemnejad (@kazemnejad)
• Dive into Deep Learning > Attention Mechanisms and Transformers
• Harvard NLP > The Annotated Transformer
• Towards Data Science > Transformers Explained Visually: Part 1, Part 2, Part 3 and Part 4 - by Ketan Doshi
• Lecture 12 of the "Deep Learning at the Vrije Universiteit Amsterdam" (DLVU) Series - by Peter Bloem (@pbloem)
• Natural Language Processing in Action Using Transformers in TensorFlow 2.0 - by Aurélien Geron (@ageron)
• TensorFlow Tutorials > Neural machine translation with a Transformer and Keras