Cloud Computing for Machine Learning and Cognitive Applications: A Comprehensive Guide by Kai Hwang
Cloud computing is a paradigm that enables the delivery of computing resources and services over the Internet. It offers many benefits, such as scalability, elasticity, cost-efficiency, and reliability. However, cloud computing also poses many challenges, such as security, privacy, performance, and interoperability.
Machine learning and cognitive applications are emerging fields that aim to create intelligent systems that can learn from data and interact with humans. They require high-performance computing, large-scale data processing, and sophisticated algorithms. Cloud computing can provide an ideal platform for developing and deploying such applications, but it also requires new techniques and methods to address the specific needs and challenges of these domains.
In this book, Kai Hwang, a renowned expert in computer architecture and parallel processing, provides a comprehensive guide to cloud computing for machine learning and cognitive applications. He covers the fundamentals of cloud computing, such as cloud models, architectures, services, and standards. He also discusses the state-of-the-art technologies and practices for designing, implementing, and optimizing cloud-based machine learning and cognitive applications, such as deep learning, natural language processing, computer vision, speech recognition, and cognitive computing. He illustrates the concepts and techniques with real-world examples and case studies from various domains, such as healthcare, education, finance, and social media.
The book is intended for students, researchers, practitioners, and professionals who are interested in learning about cloud computing for machine learning and cognitive applications. It assumes a basic knowledge of computer science and mathematics, but does not require any prior experience with cloud computing or machine learning. The book is suitable for self-study or as a textbook for advanced undergraduate or graduate courses.
Cloud Computing for Machine Learning and Cognitive Applications is published by MIT Press in 2017. It has 624 pages, 273 black-and-white illustrations, and 98 tables. It is available in hardcover and ebook formats. The book has received positive reviews from experts and readers alike. It is praised for its comprehensive coverage, clear presentation, practical examples, and insightful analysis.
If you want to learn more about cloud computing for machine learning and cognitive applications, you can order the book from MIT Press website[^2^] or other online retailers.
In the first part of the book, Hwang introduces the basic concepts and principles of cloud computing. He explains the cloud models, such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS). He also describes the cloud architectures, such as public, private, hybrid, and community clouds. He then discusses the cloud services, such as storage, computation, communication, and security. He also presents the cloud standards, such as OpenStack, CloudStack, and Apache Hadoop.
In the second part of the book, Hwang focuses on the machine learning and cognitive applications in the cloud. He reviews the main techniques and methods of machine learning, such as supervised learning, unsupervised learning, reinforcement learning, and deep learning. He also introduces the main domains and tasks of cognitive applications, such as natural language processing, computer vision, speech recognition, and cognitive computing. He then shows how to design, implement, and optimize cloud-based machine learning and cognitive applications using various tools and frameworks, such as TensorFlow, PyTorch, Spark MLlib, and IBM Watson.
In the third part of the book, Hwang presents some real-world examples and case studies of cloud-based machine learning and cognitive applications. He demonstrates how to apply cloud computing to various domains and scenarios, such as healthcare analytics, education assessment, financial fraud detection, and social media analysis. He also analyzes the benefits and challenges of cloud computing for machine learning and cognitive applications in terms of performance, scalability, reliability, security, privacy, and cost. aa16f39245