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harvardnlp.github.io | ||
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comsci.blog
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
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
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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golb.hplar.ch
[AI summary] The blog post details the author's experience implementing a feedforward neural network for digit recognition using Java and JavaScript, explaining the underlying algorithms, shared external libraries, and architectural decisions while reviewing an introductory book on the topic. |
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