LEADER 03520nam 22006255 450 001 9911007471103321 005 20250522130240.0 010 $a981-9647-06-1 024 7 $a10.1007/978-981-96-4706-4 035 $a(MiAaPQ)EBC32128678 035 $a(Au-PeEL)EBL32128678 035 $a(CKB)38874963000041 035 $a(DE-He213)978-981-96-4706-4 035 $a(EXLCZ)9938874963000041 100 $a20250522d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe Geometry of Intelligence: Foundations of Transformer Networks in Deep Learning /$fby Pradeep Singh, Balasubramanian Raman 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (467 pages) 225 1 $aStudies in Big Data,$x2197-6511 ;$v175 311 08$a981-9647-05-3 327 $aFoundations 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. 330 $aThis book offers an in-depth exploration of the mathematical foundations underlying transformer networks, the cornerstone of modern AI across various domains. Unlike existing literature that focuses primarily on implementation, this work delves into the elegant geometry, symmetry, and mathematical structures that drive the success of transformers. Through rigorous analysis and theoretical insights, the book unravels the complex relationships and dependencies that these models capture, providing a comprehensive understanding of their capabilities. Designed for researchers, academics, and advanced practitioners, this text bridges the gap between practical application and theoretical exploration. Readers will gain a profound understanding of how transformers operate in abstract spaces, equipping them with the knowledge to innovate, optimize, and push the boundaries of AI. Whether you seek to deepen your expertise or pioneer the next generation of AI models, this book is an essential resource on the mathematical principles of transformers. 410 0$aStudies in Big Data,$x2197-6511 ;$v175 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aTelecommunication 606 $aMachine learning 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aCommunications Engineering, Networks 606 $aMachine Learning 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aTelecommunication. 615 0$aMachine learning. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aCommunications Engineering, Networks. 615 24$aMachine Learning. 676 $a006.3 700 $aSingh$b Pradeep$01782429 701 $aRaman$b Balasubramanian$01355715 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911007471103321 996 $aThe Geometry of Intelligence: Foundations of Transformer Networks in Deep Learning$94392936 997 $aUNINA