Transformers Components -

Since Transformers do not process data sequentially like RNNs, they must explicitly "learn" the order of words.

: This involves running multiple self-attention operations in parallel, which helps the model capture diverse relationships within the data. 3. Feed-Forward Neural Networks (FFN)

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

The is a deep learning architecture that relies on parallelized attention mechanisms rather than sequential recurrence. Its primary components are organized into an Encoder and a Decoder , which work together to transform input sequences into contextualized representations and subsequently into output sequences. 1. Input Processing: Embedding & Positional Encoding Since Transformers do not process data sequentially like

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

: These convert discrete tokens (words or characters) into fixed-size vectors that capture initial semantic meaning. Feed-Forward Neural Networks (FFN) This is the "core"

: 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