Transformer Model Components
The discussion delves into the architecture of transformer models, highlighting the distinction between decoder-only models and the addition of encoders. By comparing the encoder to a four-story building, key components such as input embedding and self-attention mechanisms are outlined, emphasizing their role in enhancing model functionality. The conversation also touches on the importance of class outputs in models like BERT, illustrating how these concepts apply across different types of data.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
759: Full Encoder-Decoder Transformers Fully Explained — with Kirill Eremenko
Related Questions