東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

GPU-accelerated deep learning = essential GPU ideas, deep learning frameworks, and optimization approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	GPU-accelerated deep learning/ by Ramchandra S Mangrulkar, Pallavi Vijay Chavan.
Reminder of title:	essential GPU ideas, deep learning frameworks, and optimization approaches /
Author:	Mangrulkar, Ramchandra.
other author:	Chavan, Pallavi Vijay.
Published:	Berkeley, CA :Apress : : 2025.,
Description:	xix, 146 p. :ill., digital ;24 cm.
[NT 15003449]:	1 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.
Contained By:	Springer Nature eBook
Subject:	Deep learning (Machine learning) -
Online resource:	https://doi.org/10.1007/979-8-8688-2083-0
ISBN:	9798868820830

GPU-accelerated deep learning = essential GPU ideas, deep learning frameworks, and optimization approaches /
Mangrulkar, Ramchandra.

GPU-accelerated deep learningessential GPU ideas, deep learning frameworks, and optimization approaches /[electronic resource] :by Ramchandra S Mangrulkar, Pallavi Vijay Chavan. - Berkeley, CA :Apress :2025. - xix, 146 p. :ill., digital ;24 cm.

1 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.

Explore 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.

ISBN: 9798868820830

Standard No.: 10.1007/979-8-8688-2083-0doiSubjects--Topical Terms:

3538509
Deep learning (Machine learning)

LC Class. No.: Q325.73

Dewey Class. No.: 006.31

GPU-accelerated deep learning = essential GPU ideas, deep learning frameworks, and optimization approaches /
LDR:02505nmm a2200325 a 4500 001 2422987
003 DE-He213
005 20260102123005.0
006 m d
007 cr nn 008maaau
008 260505s2025 cau s 0 eng d
020 $a 9798868820830 $q (electronic bk.)
020 $a 9798868820823 $q (paper)
024 7 $a 10.1007/979-8-8688-2083-0 $2 doi
035 $a 979-8-8688-2083-0
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.73
072 7 $a UMP $2 bicssc
072 7 $a COM051380 $2 bisacsh
072 7 $a UMP $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.73 $b .M277 2025
100 1 $a Mangrulkar, Ramchandra. $3 3713942
245 1 0 $a GPU-accelerated deep learning $h [electronic resource] : $b essential GPU ideas, deep learning frameworks, and optimization approaches / $c by Ramchandra S Mangrulkar, Pallavi Vijay Chavan.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2025.
300 $a xix, 146 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1 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.
520 $a Explore 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.
650 0 $a Deep learning (Machine learning) $3 3538509
650 0 $a Graphics processing units. $3 2131960
650 1 4 $a Microsoft. $3 3593799
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Chavan, Pallavi Vijay. $3 3730067
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/979-8-8688-2083-0
950 $a Professional and Applied Computing (SpringerNature-12059)