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nlp.seas.harvard.edu | ||
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comsci.blog
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| | | | | In this tutorial, we will implement transformers step-by-step and understand their implementation. There are other great tutorials on the implementation of transformers, but they usually dive into the complex parts too early, like they directly start implementing additional parts like masks and multi-head attention, but it is not very intuitional without first building the core part of the transformers. | |
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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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teddykoker.com
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| | | | | This post is the first in a series of articles about natural language processing (NLP), a subfield of machine learning concerning the interaction between computers and human language. This article will be focused on attention, a mechanism that forms the backbone of many state-of-the art language models, including Googles BERT (Devlin et al., 2018), and OpenAIs GPT-2 (Radford et al., 2019). | |
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sander.ai
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| | | Perspectives on diffusion, or how diffusion models are autoencoders, deep latent variable models, score function predictors, reverse SDE solvers, flow-based models, RNNs, and autoregressive models, all at once! | ||
