Transformers from scratch | peterbloem.nl

I will assume a basic understanding of neural networks and backpropagation. If you’d like to brush up, this lecture will give you the basics of neural networks and this one will explain how these principles are applied in modern deep learning systems. A working knowledge of Pytorch is required to understand the programming examples, but these can also be safely skipped. The fundamental operation of any transformer architecture is the self-attention operation. Self-attention is a sequence-to-sequence operation: a sequence of vectors goes in, and a sequence of vectors comes out. Let’s call the input vectors 𝐱 1 , 𝐱 2 ,…, 𝐱 t and the corresponding output vectors 𝐲 1 , 𝐲 2 ,…, 𝐲 t . The vectors all have dimension k . To produce output vector 𝐲 i , the self attention operation simply takes a weighted average over all the input vectors 𝐲 i = ∑ j w ij 𝐱 j . Where j indexes over the whole sequence and the weights sum to one over all j . The weight w ij is not a parameter, as

18 Aug 2019 code on codeberg video lecture Transformers are a very exciting family of machine learning architectures. Many good tutorials exist (e.g. [1, 2]) but in the last few years, transformers have mostly become simpler, so that it is now much more straightforward to explain how modern architectures work. This post is an attempt to explain directly how modern transformers work, and why, without some of the historical baggage. I will assume a basic understanding of neural networks and backpropagation. If you’d like to brush up, this lecture will give you the basics of neural networks and t

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