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28min chapter
759: Full Encoder-Decoder Transformers Fully Explained, with Kirill Eremenko
Super Data Science: ML & AI Podcast with Jon Krohn
Understanding Transformer Models in Natural Language Processing
The chapter provides a detailed explanation of transformer models in NLP, focusing on the process of encoding separate English and Spanish words, utilizing self-attention and cross-attention mechanisms to create context-rich vectors for translation. It discusses the differences between encoder-only architectures like BERT and encoder-decoder structures, emphasizing the benefits of using both encoder and decoder for tasks like text generation and classification. The conversation explores the efficiency of using a full transformer architecture with separate encoder and decoder for tasks like translation, highlighting the importance of masking in attention mechanisms for accurate predictions.
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