Understanding Transformers Part 8: Shared Weights in Self-Attention

1. 1Calculate the query that represents the word 'go' using the input embeddings
2. 2Use the pre-calculated keys and values to compute the self-attention values
3. 3Apply the self-attention mechanism to the query, keys, and values to obtain the weighted sum
4. 4Visualize the self-attention weights to understand the relationships between the input elements
5. 5Implement the self-attention mechanism using a popular deep learning library like PyTorch or TensorFlow

NLP engineers and researchers can benefit from understanding shared weights in self-attention to improve their language models

💡 Shared weights in self-attention allow the model to capture complex relationships between input elements

🤖 Understand how shared weights in self-attention work in Transformers! 📚