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nlp.seas.harvard.edu | ||
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jalammar.github.io
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| | | | | Discussions: Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments) Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Italian, Japanese, Korean, Persian, Russian, Spanish 1, Spanish 2, Vietnamese Watch: MIT's Deep Learning State of the Art lecture referencing this post Featured in courses at Stanford, Harvard, MIT, Princeton, CMU and others Update: This post has now become a book! Check out LLM-book.com which contains (Chapter 3) an updated and expanded version of this post speaking about the latest Transformer models and how they've evolved in the seven years since the original Transformer (like Multi-Query Attention and RoPE Positional embeddings). In the previous post, we looked at Att... | |
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harvardnlp.github.io
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| | | | | [AI summary] The provided code is a comprehensive implementation of the Transformer model, including data loading, model architecture, training, and visualization. It also includes functions for decoding and visualizing attention mechanisms across different layers of the model. The code is structured to support both training and inference, with examples provided for running the model and visualizing attention patterns. | |
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swethatanamala.github.io
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| | | | | In this paper, authors proposed a new simple network architecture, the Transformer, based solely on attention mechanisms, removing convolutions and recurrences entirely. Transformer is the first transduction model relying entirely... | |
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www.khanna.law
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| | | You want to train a deep neural network. You have the data. It's labeled and wrangled into a useful format. What do you do now? | ||
