Building an Encoder-Decoder Transformer from Scratch!: PyTorch Deep Learning Tutorial
In this video, we dive deep into the Encoder-Decoder Transformer architecture, a key concept in natural language processing and sequence-to-sequence modeling. If you're new here, check out my GitHub repo for all the code used in this series. Previously, we explored the Encoder-only and Decoder-only architectures, but today we're combining them to tackle next-token prediction.
The Encoder-Decoder architecture was popularized by the "Attention is All You Need" paper and is essential for tasks like language translation and text generation. We’ll break down how to implement self-attention, causal masking, and cross-attention layers in PyTorch, using the Yahoo Answers dataset for demonstration.
This video contains practical insights for anyone looking to learn Transformers, multi-headed attention, and advanced deep learning techniques. Whether you're working on NLP, chatbots, or text classification, this tutorial is for you.
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The corresponding code is available here! (Section 14)
https://github.com/LukeDitria/pytorch...
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