Amir Masoud Rahmani - Böcker
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3 produkter
3 produkter
Model Optimization Methods for Efficient and Edge AI
Federated Learning Architectures, Frameworks and Applications
Inbunden, Engelska, 2024
1 454 kr
Skickas inom 7-10 vardagar
Comprehensive overview of the fledgling domain of federated learning (FL), explaining emerging FL methods, architectural approaches, enabling frameworks, and applications Model Optimization Methods for Efficient and Edge AI explores AI model engineering, evaluation, refinement, optimization, and deployment across multiple cloud environments (public, private, edge, and hybrid). It presents key applications of the AI paradigm, including computer vision (CV) and Natural Language Processing (NLP), explaining the nitty-gritty of federated learning (FL) and how the FL method is helping to fulfill AI model optimization needs. The book also describes tools that vendors have created, including FL frameworks and platforms such as PySyft, Tensor Flow Federated (TFF), FATE (Federated AI Technology Enabler), Tensor/IO, and more. The first part of the text covers popular AI and ML methods, platforms, and applications, describing leading AI frameworks and libraries in order to clearly articulate how these tools can help with visualizing and implementing highly flexible AI models quickly. The second part focuses on federated learning, discussing its basic concepts, applications, platforms, and its potential in edge systems (such as IoT). Other topics covered include: Building AI models that are destined to solve several problems, with a focus on widely articulated classification, regression, association, clustering, and other prediction problemsGenerating actionable insights through a variety of AI algorithms, platforms, parallel processing, and other enablersCompressing AI models so that computational, memory, storage, and network requirements can be substantially reducedAddressing crucial issues such as data confidentiality, data access rights, data protection, and access to heterogeneous dataOvercoming cyberattacks on mission-critical software systems by leveraging federated learningWritten in an accessible manner and containing a helpful mix of both theoretical concepts and practical applications, Model Optimization Methods for Efficient and Edge AI is an essential reference on the subject for graduate and postgraduate students, researchers, IT professionals, and business leaders.
Optimization Techniques for Deep Learning
Improving Performance and Efficiency
Inbunden, Engelska, 2026
1 693 kr
Kommande
This book offers a comprehensive guide to optimization techniques in deep learning, a transformative branch of artificial intelligence that has revolutionized fields from computer vision to healthcare. By bridging the gap between theoretical concepts and practical applications, it equips readers with the tools needed to harness the full potential of deep neural networks.The chapters cover a wide range of optimization methods, beginning with the fundamentals of neural networks and key concepts of deep learning. Readers will explore critical topics such as gradient descent, stochastic optimization, and advanced algorithms, while also addressing the inherent challenges of optimization. The book delves into practical aspects, offering insights into how to make training deep models more efficient and stable. Emerging trends and future perspectives are also presented, making this work a must-read for anyone looking to stay at the forefront of the field.This book is an invaluable resource for researchers and practitioners seeking practical solutions for optimizing neural networks. Students will find a clear path to understanding the principles and building theoretical knowledge, while industry professionals will gain insights into the latest techniques and trends. Whether you're a seasoned expert or new to the field, this book is essential for anyone interested in deep learning optimization.
Quantum Generative Adversarial Networks
GANs, Semi-Supervised GANs, Variational Autoencoders
Inbunden, Engelska, 2026
2 010 kr
Skickas inom 3-6 vardagar
GANs represent a generative model leveraging the growing power of deep learning (DL) algorithms. This book presents the breakthroughs, applications and latest research innovations of GANs and of quantum GANs. In particular, the book includes contributions on Autoencoders and Variational Autoencoders, Semi-Supervised GANs, Quantum GANs and Quantum Data Privacy, and Quantum GANs for image manipulation.