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Tag: Distributed
Distributed Applications and Interoperable Systems (Lecture Notes in Computer Science)
Price:$64.99– $5.20
(as of Dec 26,2024 14:12:48 UTC – Details)
Publisher : Springer; 1st ed. 2023 edition (June 9, 2023)
Language : English
Paperback : 144 pages
ISBN-10 : 3031352599
ISBN-13 : 978-3031352591
Item Weight : 7.5 ounces
Dimensions : 6.1 x 0.33 x 9.25 inches
Distributed Applications and Interoperable Systems: Exploring the Latest Research in Computer ScienceIn the ever-evolving world of computer science, the development of distributed applications and interoperable systems is crucial for enabling seamless communication and collaboration between different devices and platforms. The Lecture Notes in Computer Science series provides a comprehensive overview of the latest research and advancements in this field.
From the design and implementation of distributed systems to the integration of diverse technologies for creating interoperable systems, this series covers a wide range of topics that are essential for building efficient and reliable software applications. Whether you are a student, researcher, or industry professional, these lecture notes offer valuable insights and practical guidance for navigating the complexities of distributed computing.
Join us as we delve into the fascinating world of distributed applications and interoperable systems, and discover how these technologies are shaping the future of computer science. Stay tuned for updates on the latest research findings and emerging trends in this dynamic and rapidly evolving field.
#Distributed #Applications #Interoperable #Systems #Lecture #Notes #Computer #Science
PyTorch Cookbook: 100+ Solutions across RNNs, CNNs, python tools, distributed tr
PyTorch Cookbook: 100+ Solutions across RNNs, CNNs, python tools, distributed tr
Price :65.41– 54.51
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aining, and moreAre you looking for a comprehensive guide to PyTorch that covers a wide range of topics including recurrent neural networks (RNNs), convolutional neural networks (CNNs), advanced Python tools, distributed training, and more? Look no further than the PyTorch Cookbook!
With over 100 solutions to common problems and challenges faced by PyTorch developers, this cookbook is your go-to resource for mastering the popular deep learning framework. Whether you’re a beginner looking to get started with PyTorch or an experienced practitioner looking to deepen your knowledge, this cookbook has something for everyone.
From building custom datasets and training models, to optimizing performance and deploying models to production, the PyTorch Cookbook has you covered. With step-by-step explanations, code snippets, and practical examples, you’ll learn how to tackle real-world problems using PyTorch’s powerful features.
So why wait? Dive into the world of PyTorch with the PyTorch Cookbook and take your deep learning skills to the next level!
#PyTorch #Cookbook #Solutions #RNNs #CNNs #python #tools #distributed
Guide to Cloud Computing for Business and Technology Managers: From Distributed
Guide to Cloud Computing for Business and Technology Managers: From Distributed
Price :111.47– 92.89
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Systems to Data SecurityCloud computing has become an essential component of modern business operations, offering unparalleled flexibility, scalability, and cost-efficiency. As a business or technology manager, understanding how to leverage cloud computing effectively can give your organization a competitive edge. In this comprehensive guide, we will take you through the key concepts and considerations of cloud computing, from distributed systems to data security.
1. Understanding Cloud Computing: At its core, cloud computing involves delivering computing services over the internet. These services can include storage, networking, databases, analytics, and more. Cloud computing offers on-demand access to resources, allowing organizations to scale up or down as needed without the need for physical infrastructure.
2. Types of Cloud Computing: There are three main types of cloud computing models: public cloud, private cloud, and hybrid cloud. Public cloud services are provided by third-party providers and shared among multiple organizations. Private cloud services are dedicated to a single organization and can be hosted on-site or by a third-party provider. Hybrid cloud combines elements of both public and private cloud services.
3. Benefits of Cloud Computing: Cloud computing offers numerous benefits for businesses, including cost savings, scalability, flexibility, and increased collaboration. By moving to the cloud, organizations can reduce the need for physical infrastructure, improve accessibility to data and applications, and enhance overall efficiency.
4. Distributed Systems: Cloud computing relies on distributed systems to manage and deliver services across multiple servers and data centers. Distributed systems enable organizations to achieve high availability, fault tolerance, and scalability. Understanding how distributed systems work is essential for ensuring optimal performance and reliability in the cloud.
5. Data Security: Data security is a critical consideration in cloud computing, as organizations must protect sensitive information from unauthorized access and breaches. Implementing robust security measures, such as encryption, access controls, and regular audits, is essential for safeguarding data in the cloud. Additionally, organizations should ensure compliance with industry regulations and standards to mitigate risks.
In conclusion, cloud computing offers a wealth of opportunities for businesses to streamline operations, improve efficiency, and drive innovation. By understanding the key concepts and considerations of cloud computing, business and technology managers can make informed decisions to leverage this technology effectively. From distributed systems to data security, adopting a strategic approach to cloud computing can help organizations achieve their business objectives and stay ahead in today’s competitive landscape.
#Guide #Cloud #Computing #Business #Technology #Managers #Distributed, cloud computing
Consensus In Data Management: From Distributed Commit To Blockchain by Faisal…
Consensus In Data Management: From Distributed Commit To Blockchain by Faisal…
Price : 105.40
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Consensus In Data Management: From Distributed Commit To Blockchain by FaisalConsensus in data management is crucial for ensuring the integrity and reliability of data within a system. Traditionally, consensus has been achieved through distributed commit protocols, where all nodes in a network must agree on a transaction before it is considered valid. However, with the rise of blockchain technology, a new method of achieving consensus has emerged.
Blockchain technology utilizes a decentralized network of nodes to reach consensus on the validity of transactions. Through the use of a consensus algorithm, such as Proof of Work or Proof of Stake, nodes work together to validate and add new transactions to the blockchain. This ensures that all nodes in the network have a consistent view of the data, without the need for a central authority.
The shift towards using blockchain for consensus in data management has several advantages. Firstly, it eliminates the need for a trusted third party to validate transactions, reducing the risk of fraud or manipulation. Additionally, blockchain technology provides a transparent and immutable record of all transactions, making it easier to audit and verify the integrity of the data.
Overall, the move towards using blockchain for consensus in data management represents a significant shift in how data is governed and managed. By leveraging the power of decentralized networks, organizations can ensure the integrity and reliability of their data, while also reducing the risk of fraud and manipulation.
#Consensus #Data #Management #Distributed #Commit #Blockchain #Faisal.., Data Management
Advanced Data Management: For Sql, Nosql, Cloud and Distributed Databases: New
Advanced Data Management: For Sql, Nosql, Cloud and Distributed Databases: New
Price : 50.06
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In the ever-evolving world of data management, staying up-to-date on the latest advancements and technologies is crucial. From traditional SQL databases to NoSQL, cloud, and distributed databases, the landscape of data management is constantly changing.In this post, we will explore the latest trends and strategies in advanced data management for SQL, NoSQL, cloud, and distributed databases. Whether you are a seasoned data professional or just starting out, there is always something new to learn in the world of data management.
Stay tuned as we delve into topics such as data modeling, query optimization, data security, scalability, and much more. Keep up with the latest advancements in data management and stay ahead of the curve in this fast-paced industry.
Join us on this journey of exploring the cutting-edge technologies and best practices in advanced data management for SQL, NoSQL, cloud, and distributed databases. Let’s unlock the full potential of your data and drive your business forward with the power of advanced data management.
#Advanced #Data #Management #Sql #Nosql #Cloud #Distributed #Databases, Data Management
Consensus In Data Management: From Distributed Commit To Blockchain by Faisal…
Consensus In Data Management: From Distributed Commit To Blockchain by Faisal…
Price : 105.39
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Consensus In Data Management: From Distributed Commit To Blockchain by FaisalIn the world of data management, achieving consensus among multiple parties is crucial for ensuring data integrity and reliability. Traditional methods of achieving consensus, such as distributed commit protocols, have been widely used but are not without their limitations. With the rise of blockchain technology, a new approach to achieving consensus in data management has emerged.
Blockchain technology offers a decentralized and secure way of reaching consensus among multiple parties. By using cryptographic algorithms and distributed ledger technology, blockchain allows for data to be securely recorded and verified by a network of participants, without the need for a central authority.
One of the key advantages of blockchain technology is its immutability, meaning that once data is recorded on the blockchain, it cannot be altered or tampered with. This provides a level of trust and transparency that is unmatched by traditional data management systems.
While blockchain technology is still relatively new, its potential applications in data management are vast. From supply chain management to financial transactions, blockchain offers a secure and efficient way of achieving consensus among multiple parties.
As we continue to explore the possibilities of blockchain technology, it is clear that the traditional methods of achieving consensus in data management are being challenged. With its decentralized and secure nature, blockchain has the potential to revolutionize the way we manage and secure data in the digital age.
#Consensus #Data #Management #Distributed #Commit #Blockchain #Faisal.., Data Management
Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)
Price: $44.99
(as of Dec 24,2024 23:07:22 UTC – Details)
Publisher : Springer; 1st ed. 2023 edition (November 26, 2022)
Language : English
Hardcover : 140 pages
ISBN-10 : 3031190661
ISBN-13 : 978-3031190667
Item Weight : 15.2 ounces
Dimensions : 6.38 x 0.39 x 9.29 inches
Optimization Algorithms for Distributed Machine Learning (Synthesis Lectures on Learning, Networks, and Algorithms)Distributed machine learning has become increasingly popular due to the growing volume of data and the need to process it efficiently. In this post, we will explore the different optimization algorithms that are used in distributed machine learning, as discussed in the book “Optimization Algorithms for Distributed Machine Learning” by Francesco Orabona and Sashank J. Reddi.
The book provides a comprehensive overview of various optimization algorithms that are specifically designed for distributed machine learning. It covers a range of topics, including optimization theory, distributed computing, and machine learning algorithms.
Some of the key topics covered in the book include:
– Distributed optimization algorithms such as distributed gradient descent, distributed proximal methods, and distributed primal-dual methods.
– Communication-efficient algorithms for distributed optimization, which aim to minimize the amount of communication between nodes in a distributed system.
– Parallel and distributed algorithms for large-scale machine learning problems, such as deep learning and reinforcement learning.Overall, the book provides a valuable resource for researchers and practitioners working in the field of distributed machine learning. It offers a deep dive into the various optimization algorithms that are used in distributed settings, helping readers understand the challenges and opportunities in this rapidly evolving field.
If you are interested in learning more about optimization algorithms for distributed machine learning, be sure to check out this insightful book. It is a must-read for anyone looking to enhance their knowledge and skills in this area.
#Optimization #Algorithms #Distributed #Machine #Learning #Synthesis #Lectures #Learning #Networks #Algorithms
Learning Ray: Flexible Distributed Python for Machine Learning
Price:$65.99– $44.99
(as of Dec 24,2024 18:45:18 UTC – Details)
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O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.
Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.
Publisher : O’Reilly Media; 1st edition (March 21, 2023)
Language : English
Paperback : 271 pages
ISBN-10 : 1098117220
ISBN-13 : 978-1098117221
Item Weight : 1 pounds
Dimensions : 6.9 x 0.5 x 9.1 inches
Learning Ray: Flexible Distributed Python for Machine LearningRay is a powerful open-source framework that makes it easy to build and scale distributed Python applications. With Ray, you can easily parallelize and distribute your Python code across multiple CPUs or GPUs, making it ideal for machine learning tasks that require large amounts of computational power.
One of the key features of Ray is its flexibility. You can use Ray to build a wide range of distributed applications, from simple parallel loops to complex machine learning pipelines. Ray’s flexible API allows you to easily scale your code to take advantage of multiple cores or multiple machines, making it easy to handle large datasets and complex algorithms.
Ray is also designed to be fast and efficient, with a lightweight architecture that minimizes overhead and maximizes performance. This makes Ray a great choice for building high-performance machine learning models that require large amounts of computation.
If you’re looking to scale up your machine learning projects and take advantage of distributed computing, Ray is definitely worth checking out. Its flexibility, performance, and ease of use make it a powerful tool for building distributed Python applications for machine learning.
#Learning #Ray #Flexible #Distributed #Python #Machine #Learning
Rollout, Policy Iteration, and Distributed Reinforcement Learning
Price: $87.22
(as of Dec 24,2024 13:48:19 UTC – Details)
Rollout, Policy Iteration, and Distributed Reinforcement LearningReinforcement learning is a powerful technique in machine learning that allows agents to learn how to make decisions by interacting with an environment and receiving rewards or penalties. Three key concepts in reinforcement learning are rollout, policy iteration, and distributed reinforcement learning.
Rollout is a technique used in reinforcement learning to simulate the possible future states and rewards that an agent may encounter by taking a specific action. By performing rollouts, agents can estimate the expected value of taking a particular action in a given state, which helps them make better decisions.
Policy iteration is a method used to improve the policy (the strategy or set of rules) that an agent uses to make decisions in a reinforcement learning setting. In policy iteration, the agent evaluates its current policy by performing rollouts and then updates the policy to maximize the expected rewards. This process is repeated until the policy converges to an optimal one.
Distributed reinforcement learning is a technique that involves training multiple agents in parallel on different parts of an environment or task. By distributing the learning process, agents can explore more of the state space and learn faster than if they were trained individually. Distributed reinforcement learning is particularly useful for complex tasks that require a large amount of computation.
By combining rollout, policy iteration, and distributed reinforcement learning, researchers and practitioners can develop more efficient and effective reinforcement learning algorithms that can tackle a wide range of challenging problems. These techniques are essential for advancing the field of reinforcement learning and building intelligent systems that can learn and adapt in complex environments.
#Rollout #Policy #Iteration #Distributed #Reinforcement #Learning
Federated Learning Systems: Towards Privacy-Preserving Distributed AI (Studies in Computational Intelligence, 832)
Price:$179.99– $170.99
(as of Dec 24,2024 11:20:58 UTC – Details)
Publisher : Springer (April 10, 2025)
Language : English
ISBN-10 : 3031788400
ISBN-13 : 978-3031788406
Item Weight : 1.74 pounds
Federated Learning Systems: Towards Privacy-Preserving Distributed AI (Studies in Computational Intelligence, 832)In today’s digital age, the collection and analysis of data have become essential for the development of artificial intelligence (AI) systems. However, concerns about privacy and data security have led to the development of new approaches to training AI models without compromising user privacy. Federated learning systems have emerged as a promising solution to this challenge.
The book “Federated Learning Systems: Towards Privacy-Preserving Distributed AI” explores the concept of federated learning and its potential applications in various domains. Written by experts in the field, this book provides a comprehensive overview of the latest research and developments in federated learning systems.
Federated learning allows AI models to be trained locally on individual devices or servers, with only the model updates being shared with a central server. This decentralized approach ensures that sensitive data remains on the user’s device, preserving privacy while still enabling the training of robust AI models.
This book delves into the technical aspects of federated learning, including optimization algorithms, communication protocols, and privacy-preserving techniques. It also discusses real-world applications of federated learning, such as healthcare, finance, and smart cities.
Whether you are a researcher, developer, or practitioner in the field of AI and machine learning, “Federated Learning Systems: Towards Privacy-Preserving Distributed AI” is a valuable resource for understanding the potential of federated learning in building privacy-preserving AI systems. Stay ahead of the curve and explore the future of distributed AI with this insightful book.
#Federated #Learning #Systems #PrivacyPreserving #Distributed #Studies #Computational #Intelligence
