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This year, we saw a dazzling application of machine learning. The OpenAI GPT-2 exhibited impressive ability of writing coherent and passionate essays that exceed what we anticipated current language models are able to produce. The GPT-2 wasn’t a particularly novel architecture – it’s architecture is very similar to the decoder-only transformer. The GPT2 was, however, a very large, transformer-based language model trained on a massive dataset. In this post, we’ll look at the architecture that enabled the model to produce its results. We will go into the depths of its self-attention layer. And then we’ll look at applications for the decoder-only transformer beyond language modeling.
My goal here is to also supplement my earlier post, The Illustrated Transformer, with more visuals explaining the inner
A deep dive into Transformer a neural network architecture that was introduced in the famous paper “attention is all you need” in 2017, its applications, impacts, challenges and future directions