Attention Is All You Need

5 Steps

1. 1

   Grasp Self-Attention: Understand how Query, Key, and Value matrices interact to calculate attention scores and weighted sums, enabling the model to focus on relevant input parts. Focus on the core formula: Attention(Q, K, V) = softmax(QKᵀ/√d_k)V.

2. 2

   Implement Scaled Dot-Product Attention: Run the provided Python starter code to see a basic self-attention mechanism in action. Experiment with input dimensions and observe how output is a weighted sum of value vectors based on query-key similarity.

3. 3

   Explore Positional Encoding: Learn why positional information is crucial for Transformers and how sine/cosine functions are used to encode sequence order into input embeddings, as the architecture itself lacks recurrence.

4. 4

   Visualize the Encoder-Decoder Structure: Map out the multi-head attention, feed-forward layers, and residual connections within both the encoder and decoder blocks. Understand how the encoder processes input and the decoder generates output using encoder outputs.

5. 5

   Recognize Parallel Computation Benefits: Identify how the lack of sequential dependencies in self-attention, unlike RNNs, allows for simultaneous processing of all input tokens. Understand how this design drastically accelerates training times for large datasets.