Illustrated Guide to Transformers- Step by Step Explanation | by Michael Phi | Towards Data Science

Transformers are taking the natural language processing world by storm. These incredible models are breaking multiple NLP records and pushing the state of the art. They are used in many applications like machine language translation, conversational chatbots, and even to power better search engines. Transformers are the rage in deep learning nowadays, but how do they work? Why have they outperform the previous king of sequence problems, like recurrent neural networks, GRU’s, and LSTM’s? You’ve probably heard of different famous transformers models like BERT, GPT, and GPT2. In this post, we’ll focus on the one paper that started it all, “Attention is all you need”. Check out the link below if you’d like to watch the video version instead. To understand transformers we first must understand the attention mechanism. The Attention mechanism enables the transformers to have extremely long term memory. A transformer model can “attend” or “focus” on all previous tokens that have been generated

Transformers Explained Visually (Part 1): Overview of Functionality | Towards Data Science Skip to content Artificial Intelligence Transformers Explained Visually (Part 1): Overview of Functionality A Gentle Guide to Transformers for NLP, and why they are better than RNNs, in Plain English. How Attention helps improve performance. Ketan Doshi Dec 13, 2020 11 min read Share INTUITIVE TRANSFORMERS SERIES NLP Photo by Arseny Togulev on Unsplash We’ve been hearing a lot about Transformers and with good reason. They have taken the world of NLP by storm in the last few years. The Transformer i

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