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3 posts about Transformer by 3 contributors.
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This article explores the Transformer architecture, the backbone of modern LLMs like GPT and BERT, detailing key components such as self-attention, feedforward layers, positional encoding, and training mechanisms.
A Transformer is a deep learning model architecture designed to handle sequential data, such as text, by using mechanisms called self-attention and positional encoding instead of relying on recurrence like LSTM or GRU models.
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