The Illustrated Transformer
Jay Alammar's blog explores The Transformer model, highlighting its attention mechanism for faster training. It outperforms Google's NMT in some tasks, emphasizing parallelizability. The blog simplifies components like self-attention and multi-headed attention for better understanding.
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Jay Alammar's blog delves into the concept of The Transformer, a model utilizing attention to enhance training speed in deep learning models. The Transformer surpasses Google's Neural Machine Translation model in certain tasks and is highly parallelizable, making it Google Cloud's recommended reference model for their Cloud TPU offering. The blog simplifies the model's components, detailing the encoding and decoding components, self-attention layers, and feed-forward neural networks. It explains the process of self-attention, where each word's representation is influenced by other words in the input sequence, aiding in better encoding. The post breaks down the self-attention calculation from vectors to matrices, introducing multi-headed attention to improve the model's ability to focus on different positions and maintain various representation subspaces. The blog aims to make complex machine learning concepts accessible to a broader audience by providing visualizations and explanations of each component's functionality within The Transformer model.
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7 commentsI had a lot of trouble understand what was going on from just the original publication[0].
My favorite article on the latter is Cosma Shalizi's excellent post showing that all "attention" is really doing is kernel smoothing [0]. Personally having this 'click' was a bigger insight for me than walking through this post and implementing "attention is all you need".
In a very real sense transformers are just performing compression and providing a soft lookup functionality on top of an unimaginably large dataset (basically the majority of human writing). This understanding of LLMs helps to better understand their limitations as well as their, imho untapped, usefulness.
0. http://bactra.org/notebooks/nn-attention-and-transformers.ht...
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