LEADER 03157nam 22005295 450 001 9911049175503321 005 20260102123005.0 010 $a979-88-6882-083-0 024 7 $a10.1007/979-8-8688-2083-0 035 $a(CKB)44770035300041 035 $a(MiAaPQ)EBC32470716 035 $a(Au-PeEL)EBL32470716 035 $a(CaSebORM)9798868820830 035 $a(OCoLC)1568054109 035 $a(OCoLC-P)1568054109 035 $a(DE-He213)979-8-8688-2083-0 035 $a(EXLCZ)9944770035300041 100 $a20260102d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGPU-Accelerated Deep Learning $eEssential GPU Ideas, Deep Learning Frameworks, and Optimization Approaches /$fby Ramchandra S Mangrulkar, Pallavi Vijay Chavan 205 $a1st ed. 2025. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2025. 215 $a1 online resource (166 pages) 225 0 $aProfessional and Applied Computing Series 300 $aDescription based upon print version of record. 311 08$a979-88-6882-082-3 327 $a1 Introduction to Deep Learning and GPU Acceleration -- 2 Convolutional Neural Networks (CNNs) with GPU Optimization -- 3 Sequence Models and Recurrent Networks -- 4 Generative Models and integration with Microsoft Copilots -- 5 Deployment on Edge Devices -- 6 Scaling and Distributed Training. 330 $aExplore the convergence of deep learning and GPU technology. This book is a complete guide for those wishing to use GPUs to accelerate AI workflows. The book is meant to make complex concepts understandable, with step-by-step instructions on how to set up and use GPUs in deep learning applications. Starting with an introduction to the fundamentals, you'll dive into progressive topics like Convolutional Neural Networks (CNNs) and sequence models, exploring how GPU optimization boosts performance. Further, you will learn the power of generative models, and take your skills by deploying AI models on edge devices. Finally, you will master the art of scaling and distributed training to handle large datasets and complex tasks efficiently. This book is your roadmap to becoming proficient in deep learning and harnessing the full potential of GPUs. What You Will Learn: How to apply deep learning techniques on GPUs to solve challenging AI problems. Optimizing neural networks for faster training and inference on GPUs Integration of GPUs with Microsoft Copilots Implementing VAEs (Variational Autoencoders) with TensorFlow and PyTorch. 410 0$aProfessional and Applied Computing Series 606 $aDeep learning (Machine learning) 606 $aGraphics processing units 615 0$aDeep learning (Machine learning) 615 0$aGraphics processing units. 676 $a006.3/1 700 $aMangrulkar$b Ramchandra S$01784395 701 $aChavan$b Pallavi Vijay$01886039 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911049175503321 996 $aGPU-Accelerated Deep Learning$94521437 997 $aUNINA