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Mathematical Foundations and Transformer Principles
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The Transformer Principles Series is a three-volume graduate-level treatise that builds a complete mathematical and engineering understanding of modern AI systems, from the foundational attention mechanism to large language models and multimodal architectures.
Volume I - Mathematical Foundations and Transformer Principles begins with the historical evolution from symbolic AI to deep learning, then develops the essential mathematics: linear algebra, probability, optimization, neural network backpropagation, and information theory. These tools are applied through a systematic construction of the Transformer - self-attention, multi-head projections, positional encodings, feed-forward networks, residual connections, and normalization - culminating in the complete encoder-decoder architecture and an exploration of efficient attention variants, mixture-of-experts, and state-space models.
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