Transformers: Components

Following the attention layers, each position in the encoder and decoder is processed by a .

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 transformers components

This is the "core" of the architecture, allowing the model to focus on different parts of the input sequence simultaneously.

: Converts these raw scores into a probability distribution, allowing the model to select the most likely next token. Following the attention layers, each position in the

: Normalizes the vector features to keep activations at a consistent scale, preventing vanishing or exploding gradients.

: 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 : Normalizes the vector features to keep activations

This consists of two linear transformations with a non-linear activation (typically ReLU) in between.