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harvardnlp.github.io | ||
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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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nlp.seas.harvard.edu
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| | | | | The Annotated Transformer | |
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sigmoidprime.com
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| | | | | An exploration of Transformer-XL, a modified Transformer optimized for longer context length. | |
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blog.fastforwardlabs.com
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| | | This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra. | ||
