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The Geometry of Intelligence: Foundations of Transformer Networks in Deep Learning / by Pradeep Singh, Balasubramanian Raman.
Springer eBooks EBA - Intelligent Technologies and Robotics Collection 2025 Available online
View online- Format:
- Book
- Author/Creator:
- Singh, Pradeep.
- Series:
- Studies in Big Data, 2197-6511 ; 175
- Language:
- English
- Subjects (All):
- Computational intelligence.
- Artificial intelligence.
- Telecommunication.
- Machine learning.
- Computational Intelligence.
- Artificial Intelligence.
- Communications Engineering, Networks.
- Machine Learning.
- Local Subjects:
- Computational Intelligence.
- Artificial Intelligence.
- Communications Engineering, Networks.
- Machine Learning.
- Physical Description:
- 1 online resource (467 pages)
- Edition:
- 1st ed. 2025.
- Place of Publication:
- Singapore : Springer Nature Singapore : Imprint: Springer, 2025.
- Summary:
- This book offers an in-depth exploration of the mathematical foundations underlying transformer networks, the cornerstone of modern AI across various domains. Unlike existing literature that focuses primarily on implementation, this work delves into the elegant geometry, symmetry, and mathematical structures that drive the success of transformers. Through rigorous analysis and theoretical insights, the book unravels the complex relationships and dependencies that these models capture, providing a comprehensive understanding of their capabilities. Designed for researchers, academics, and advanced practitioners, this text bridges the gap between practical application and theoretical exploration. Readers will gain a profound understanding of how transformers operate in abstract spaces, equipping them with the knowledge to innovate, optimize, and push the boundaries of AI. Whether you seek to deepen your expertise or pioneer the next generation of AI models, this book is an essential resource on the mathematical principles of transformers.
- Contents:
- Foundations of Representation Theory in Transformers
- Word Embeddings and Positional Encoding
- Attention Mechanisms
- Transformer Architecture: Encoder and Decoder
- Transformers in Natural Language Processing
- Transformers in Computer Vision
- Time Series Forecasting with Transformers
- Signal Analysis and Transformers
- Advanced Topics and Future Directions
- Convergence of Transformer Models: A Dynamical Systems Perspective.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 981-9647-06-1
- OCLC:
- 1521496113
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