LEADER 01381nam1 22003253i 450 001 VAN00257846 005 20240806101452.308 100 $a20230505f1900 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aXenophontis Scripta minora$frecognovit Ludovicus Dindorfius 210 $aLipsiae$cin aedibus B. G. Teubneri 215 $avolumi$d19 cm 463 \1$1001VAN00257844$12001 $a<<1:>>Oeconomicum, Convivium, Hieronem, Agesilaum, Apologiam Socratis continens 463 \1$1001VAN00257847$12001 $a<<2: >>Rempublicam Lacedaemoniorum$aRempublicam Atheniensium ; De vectigalibus librum ; Hipparchicum ; De re equestri librum ; Cynergeticum continens 620 $dLeipzig$3VANL001016 700 0$aXenophon$3VANV009950$075253 702 1$aDindorf$bLudwig August$3VANV072231 712 $aTeubner $3VANV109204$4650 790 0$aSenofonte$zXenophon$3VANV055312 790 0$aXenophon : Ephesius$zXenophon$3VANV209281 790 0$aXenophon : Atheniensis$zXenophon$3VANV209283 790 1$aDindorf, Ludovicus$zDindorf, Ludwig August$3VANV072232 790 1$aDindorfius, Ludovicus$zDindorf, Ludwig August$3VANV072233 790 1$aDindorfio, Ludovico$zDindorf, Ludwig August$3VANV072234 801 $aIT$bSOL$c20250131$gRICA 912 $aVAN00257846 996 $aXenophontis scripta minora$9912717 997 $aUNICAMPANIA LEADER 04106nam 22006135 450 001 9910921007003321 005 20250723201726.0 010 $a9789819780198 010 $a9819780195 024 7 $a10.1007/978-981-97-8019-8 035 $a(CKB)37133693400041 035 $a(MiAaPQ)EBC31874096 035 $a(Au-PeEL)EBL31874096 035 $a(OCoLC)1482832816 035 $a(DE-He213)978-981-97-8019-8 035 $a(EXLCZ)9937133693400041 100 $a20250103d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning Through the Prism of Tensors /$fby Pradeep Singh, Balasubramanian Raman 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (483 pages) 225 1 $aStudies in Big Data,$x2197-6511 ;$v162 311 08$a9789819780181 311 08$a9819780187 327 $aChapter 1: A Tensorial Perspective to Deep Learning -- Chapter 2: The Algebra and Geometry of Deep Learning -- Chapter 3: Building Blocks -- Chapter 4: Journey into Convolutions -- Chapter 5: Modeling Temporal Data -- Chapter 6: Transformer Architectures -- Chapter 7: Attention Mechanisms Beyond Transformers -- Chapter 8: Graph Neural Networks: Extending Deep Learning to Graphs -- Chapter 9: Self-Supervised and Unsupervised Learning in Deep Learning -- Chapter 10: Learning Representations via Autoencoders and Generative Models -- Chapter 11: Recent Advances and Future Perspectives. 330 $aIn the rapidly evolving field of artificial intelligence, this book serves as a crucial resource for understanding the mathematical foundations of AI. It explores the intricate world of tensors, the fundamental elements powering today's advanced deep learning models. Combining theoretical depth with practical insights, the text navigates the complex landscape of tensor calculus, guiding readers to master the principles and applications of tensors in AI. From the basics of tensor algebra and geometry to the sophisticated architectures of neural networks, including multi-layer perceptrons, convolutional, recurrent, and transformer models, this book provides a comprehensive examination of the mechanisms driving modern AI innovations. It delves into the specifics of autoencoders, generative models, and geometric interpretations, offering a fresh perspective on the complex, high-dimensional spaces traversed by deep learning technologies. Concluding with a forward-looking view, the book addresses the latest advancements and speculates on the future directions of AI research, preparing readers to contribute to or navigate the next wave of innovations in the field. Designed for academics, researchers, and industry professionals, it serves as both an essential textbook for graduate and postgraduate students and a valuable reference for experts in the field. With its rigorous approach to the mathematical frameworks of AI and a strong focus on practical applications, this book bridges the gap between theoretical research and real-world implementation, making it an indispensable guide in the realm of artificial intelligence. 410 0$aStudies in Big Data,$x2197-6511 ;$v162 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aMathematics 606 $aComputational Intelligence 606 $aArtificial Intelligence 606 $aApplications of Mathematics 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aMathematics. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aApplications of Mathematics. 676 $a006.31015163 700 $aSingh$b Pradeep$01782429 701 $aRaman$b Balasubramanian$01355715 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910921007003321 996 $aDeep Learning Through the Prism of Tensors$94308609 997 $aUNINA