Transformers (eBook, ePUB)
Principles and Applications
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"Transformers: Principles and Applications" "Transformers: Principles and Applications" offers an in-depth, comprehensive guide to the architecture, theory, and practical use of transformer models-the foundational technology that has revolutionized natural language processing and is rapidly advancing numerous fields beyond. The book opens by tracing the historical evolution from recurrent and convolutional networks to attention-based models, elucidating the core mechanisms of transformers, including self-attention, multi-head attention, positional encoding, normalization, and model scaling. Th...
"Transformers: Principles and Applications"
"Transformers: Principles and Applications" offers an in-depth, comprehensive guide to the architecture, theory, and practical use of transformer models-the foundational technology that has revolutionized natural language processing and is rapidly advancing numerous fields beyond. The book opens by tracing the historical evolution from recurrent and convolutional networks to attention-based models, elucidating the core mechanisms of transformers, including self-attention, multi-head attention, positional encoding, normalization, and model scaling. Through rigorous analyses and mathematical formalism, readers gain a robust understanding of the transformer's inner workings and the innovations that set them apart.
Spanning advanced training strategies, deployment considerations, and architectural variants, the text equips practitioners and researchers with best practices for building, optimizing, and scaling transformer models. It delves into algorithms for large-scale distributed training, memory efficiency, and transfer learning, while covering cutting-edge developments such as efficient attention mechanisms, multimodal architectures, lightweight deployments, and domain-specific adaptations for vision, speech, time-series, and scientific applications. Readers will also find thorough treatments of interpretability, safety, fairness, and ethical deployment, supported by discussions on adversarial robustness, bias mitigation, confidence calibration, and regulatory frameworks.
As transformer models continue to scale and pervade ever more domains, this book explores emerging frontiers and open challenges such as foundation models, unsupervised learning, neural architecture search, alignment and value loading, and their integration with interactive systems and embodied intelligence. "Transformers: Principles and Applications" stands as both an authoritative reference and a practical resource, empowering readers to leverage the full potential of transformers in research and real-world engineering.
"Transformers: Principles and Applications" offers an in-depth, comprehensive guide to the architecture, theory, and practical use of transformer models-the foundational technology that has revolutionized natural language processing and is rapidly advancing numerous fields beyond. The book opens by tracing the historical evolution from recurrent and convolutional networks to attention-based models, elucidating the core mechanisms of transformers, including self-attention, multi-head attention, positional encoding, normalization, and model scaling. Through rigorous analyses and mathematical formalism, readers gain a robust understanding of the transformer's inner workings and the innovations that set them apart.
Spanning advanced training strategies, deployment considerations, and architectural variants, the text equips practitioners and researchers with best practices for building, optimizing, and scaling transformer models. It delves into algorithms for large-scale distributed training, memory efficiency, and transfer learning, while covering cutting-edge developments such as efficient attention mechanisms, multimodal architectures, lightweight deployments, and domain-specific adaptations for vision, speech, time-series, and scientific applications. Readers will also find thorough treatments of interpretability, safety, fairness, and ethical deployment, supported by discussions on adversarial robustness, bias mitigation, confidence calibration, and regulatory frameworks.
As transformer models continue to scale and pervade ever more domains, this book explores emerging frontiers and open challenges such as foundation models, unsupervised learning, neural architecture search, alignment and value loading, and their integration with interactive systems and embodied intelligence. "Transformers: Principles and Applications" stands as both an authoritative reference and a practical resource, empowering readers to leverage the full potential of transformers in research and real-world engineering.
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