🧠 “Attention Is All You Need” Explained (with PyTorch from Scratch)

Author(s): MahendraMedapati

Originally published on Towards AI.

Understand the Transformer Paper Deeply & Build It Yourself Step-by-Step

In 2017, a single paper titled “Attention is All You Need” flipped the deep learning world on its head.

The article discusses the transformative impact of the “Attention is All You Need” paper in the field of Natural Language Processing (NLP), detailing the limitations of prior Recurrent Neural Networks (RNNs) and the advantages brought by the Transformer architecture. It covers essential concepts such as self-attention, positional encoding, and the functioning of encoder and decoder layers, offering a comprehensive guide on building the Transformer model in PyTorch, while emphasizing the model’s significance and implications for future developments in AI.

Read the full blog for free on Medium.

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Published via Towards AI