Following the attention layers, each position in the encoder and decoder is processed by a .
: Projects the decoder's output into a much larger vector (the size of the model's vocabulary). transformers components
: These add the original input of a layer to its output before normalization, providing a "direct path" for gradients to flow backward during training. 5. Linear and Softmax Layers Following the attention layers, each position in the
: Calculates a "relevance score" between tokens, allowing the model to understand how much focus one word should have on another (e.g., relating "he" to "Tom"). : Vectors are added to the embeddings to
These components are critical for training deep architectures by ensuring stability and gradient flow.
: Vectors are added to the embeddings to provide information about the relative or absolute position of each token in the sequence. 2. The Multi-Head Attention Mechanism
In the final stage of the decoder, the output vectors are transformed into human-readable results.
Following the attention layers, each position in the encoder and decoder is processed by a .
: Projects the decoder's output into a much larger vector (the size of the model's vocabulary).
: These add the original input of a layer to its output before normalization, providing a "direct path" for gradients to flow backward during training. 5. Linear and Softmax Layers
: Calculates a "relevance score" between tokens, allowing the model to understand how much focus one word should have on another (e.g., relating "he" to "Tom").
These components are critical for training deep architectures by ensuring stability and gradient flow.
: Vectors are added to the embeddings to provide information about the relative or absolute position of each token in the sequence. 2. The Multi-Head Attention Mechanism
In the final stage of the decoder, the output vectors are transformed into human-readable results.