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Breaking Down ‘Attention Is All You Need’: A Deep Dive into Transformers
- Transformers models like ByteNet, ConvS2S had long-term dependency issues before introducing self-attention in 'Attention Is All You Need'.
- Self-attention, a key concept in Transformers, captures relations within input sequences, unlike CNNs and RNNs.
- The Transformer model relies significantly on self-attention for processing sequences and improving accuracy.
- The model structure includes encoder and decoder parts, where encoder encodes data and decoder decodes it.
- Embedding converts words to numerical form for the machine to understand and learn patterns.
- Key components in Transformers include MultiHead Attention, Masked MultiHead Attention, Feed Forward Neural Network, etc.
- In MultiHead Attention, attention allows focusing on relevant words in a sentence by assigning different weights.
- Scaled Dot-Product Attention computes attention scores by performing dot products, scaling, and applying softmax.
- Layer Normalization stabilizes training by normalizing activations within each layer independently across all features.
- In the encoder architecture, embeddings go through multihead attention, layer normalization, and feedforward layers.
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