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Behind the Magic: How Tensors Drive Transformers
- Transformers in artificial intelligence rely on tensors to process data efficiently, enabling advancements in language understanding and data learning.
- Tensors play a crucial role in Transformers by undergoing various transformations to make sense of input data, maintain coherence, and facilitate information flow.
- The article delves into the flow of tensors within Transformer models, ensuring dimensional consistency and detailing transformations at different layers.
- The Encoder and Decoder components of Transformers process data using tensors, which undergo transformations to create useful representations and generate coherent output.
- Starting with the Input Embedding Layer, raw tokens are converted into dense vectors, maintaining semantic relationships and handling positional encoding for order preservation.
- The Multi-Head Attention mechanism, a critical part of Transformers, splits matrices into Query, Key, and Value to enable parallelization and enhance learning.
- Attention calculation involves multiple heads computing attention independently and then concatenating outputs to restore the original tensor shape post-linear transformation.
- Following the attention mechanism, a residual connection and normalization step stabilize training and maintain the tensor shape for further processing.
- The article also covers the Feed-Forward Network in decoding, utilizing Masked Multi-Head Attention and Cross-Attention to refine predictions and incorporate relevant context.
- Understanding how tensors drive Transformers aids in comprehending AI functioning, from embedding to attention mechanisms, and enhancing language comprehension and model decision-making.
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