Machine learning for cloud management, Kumar, Jitendra et al
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 9978.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Artificial intelligence is a word that carries with it heavy connotations. Although artificial intelligence is nothing more than the capacity for logic and understanding that machines can exhibit, in the minds of most people artificial intelligence is almost a Pandora's box that, when opened, will eventually signal the human race's doom..
The idea that machines pose an existential threat to human beings has been around for at least 60 years. It goes something like this: intelligent machines eventually realize the uselessness of human beings and turn against their creators. Or this: intelligent machines reduce human to cattle or even food after a dramatic war that human beings lose.
Human beings have created countless languages and writing systems that have allowed us to expand collective human knowledge over a period of thousands of years. Much of the knowledge that we utilized today, knowledge about the math, science, and the stars, originates from observations made thousands of years ago but which were recorded by writing systems, allowing this knowledge to be preserved and passed down.
Artificial intelligence has been used for many business, financial, medical, and other applications, and scientists and researchers are actively studying how these applications can be expanded to make human life simpler.
The applications of AI will be explored in this book, both the real applications to business, finance, medicine, and health and the theoretical applications. Even the sensational, perhaps exaggerated applications of AI will be explored in the context of taking a look at how AI may potentially be applied in the future. The purpose of this discussion is for the reader to understand what AI is by understanding how it is used.
Artificial intelligence is certainly a blessing at this point, but the reality that it may become a curse is not lost on some people. Understanding the full implications of AI requires a deep knowledge of what it is and where it came from.
For companies and businesses to take advantage of AI-powered and improved interactions, the conversation has to begin inside the organization. Leaders are supposed to start with the available channels and improve their smartness. From that point, they are supposed to ask key questions about engagements with customers and employees.
Here is a preview of what you will learn...
Brief history of artificial intelligence
The state of art of machine learning
Artificial neural networks applied to machine learning
Описание: Medical image fusion is a process which merges information from multiple images of the same scene. The fused image provides appended information that can be utilized for more precise localization of abnormalities. The use of medical image processing databases will help to create and develop more accurate and diagnostic tools.
Автор: Little Max A Название: Machine Learning for Signal Processing ISBN: 0198714939 ISBN-13(EAN): 9780198714934 Издательство: Oxford Academ Рейтинг: Цена: 12038.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Describes in detail the fundamental mathematics and algorithms of machine learning (an example of artificial intelligence) and signal processing, two of the most important and exciting technologies in the modern information economy. Builds up concepts gradually so that the ideas and algorithms can be implemented in practical software applications.
Описание: Dig deep into the data with a hands-on guide to machine learning with updated examples and more! Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data.
The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference. At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data.
Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to: Learn the languages of machine learning including Hadoop, Mahout, and WekaUnderstand decision trees, Bayesian networks, and artificial neural networksImplement Association Rule, Real Time, and Batch learningDevelop a strategic plan for safe, effective, and efficient machine learning By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
Put the power of AWS Cloud machine learning services to work in your business and commercial applications
Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services.
Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems.
- Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building
- Discover common neural network frameworks with Amazon SageMaker
- Solve computer vision problems with Amazon Rekognition
- Benefit from illustrations, source code examples, and sidebars in each chapter
The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.
Автор: Mohanty Sachi Nandan, Chatterjee Jyotir Moy, Mangla Monika Название: Machine Learning Approach for Cloud Data Analytics in Iot ISBN: 1119785804 ISBN-13(EAN): 9781119785804 Издательство: Wiley Рейтинг: Цена: 29771.00 р. Наличие на складе: Поставка под заказ.
Описание: Im Zeitalter des Internet of Things (IoT) erzeugen Edge-Gerдte in jedem Sekundenbruchteil gigantische Datenmengen. Dabei besteht das Hauptziel dieser Netzwerke darin, aus den gesammelten Daten sinnvolle Informationen abzuleiten. Gleichzeitig werden gewaltige Datenmengen in die Cloud Г1/4bertragen, was extrem teuer und zeitaufwдndig ist. Es ist somit notwendig, effiziente Mechanismen fГ1/4r die Verarbeitung dieser gewaltigen Datenmengen zu entwickeln, und dafГ1/4r sind effiziente Datenverarbeitungstechniken erforderlich. Nachhaltige Paradigmen wie Cloud Computing und Fog Computing tragen zu einem geschickten Umgang mit Themen wie Leistung, Speicher- und Verarbeitungskapazitдten, Wartung, Sicherheit, Effizienz, Integration, Kosten, Energieverbrauch und Latenzzeiten bei. Allerdings werden ausgefeilte Analysetools benцtigt, um die Anfragen in einer optimalen Zeit zu bearbeiten. Daher wird derzeit eifrig an der Entwicklung eines effektiven und effizienten Rahmens geforscht, um den grцГŸtmцglichen Nutzen zu erhalten. Bei der Verarbeitung der gewaltigen Datenmengen steht das maschinelle Lernen besonders hoch im Kurs und wird in zahlreichen Disziplinen angewandt, auch in den sozialen Medien. In Machine Learning Approach for Cloud Data Analytics in IoT werden sдmtliche Aspekte des IoT, des Cloud Computing und der Datenanalyse ausfГ1/4hrlich erlдutert und aus verschiedenen Perspektiven betrachtet. Das Buch prдsentiert den neuesten Stand der Forschung und fortschrittliche Themen. So erhalten die Leserinnen und Leser aktuelle Informationen und kцnnen das gesamte Spektrum der Anwendungen von IoT, Cloud Computing und Datenanalyse erfassen.
Описание: User level: Beg-Int, don`t use au middle name
Автор: Barber Название: Bayesian Reasoning and Machine Learning ISBN: 0521518148 ISBN-13(EAN): 9780521518147 Издательство: Cambridge Academ Рейтинг: Цена: 11088.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.
Описание: THE SERIES: INTELLIGENT BIOMEDICAL DATA ANALYSIS By focusing on the methods and tools for intelligent data analysis, this series aims to narrow the increasing gap between data gathering and data comprehension. Emphasis is also given to the problems resulting from automated data collection in modern hospitals, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring. In medicine, overcoming this gap is crucial since medical decision making needs to be supported by arguments based on existing medical knowledge as well as information, regularities and trends extracted from big data sets.
Описание: Machine learning allows for non-conventional and productive answers for issues within various fields, including problems related to visually perceptive computers. Applying these strategies and algorithms to the area of computer vision allows for higher achievement in tasks such as spatial recognition, big data collection, and image processing. There is a need for research that seeks to understand the development and efficiency of current methods that enable machines to see. Challenges and Applications for Implementing Machine Learning in Computer Vision is a collection of innovative research that combines theory and practice on adopting the latest deep learning advancements for machines capable of visual processing. Highlighting a wide range of topics such as video segmentation, object recognition, and 3D modelling, this publication is ideally designed for computer scientists, medical professionals, computer engineers, information technology practitioners, industry experts, scholars, researchers, and students seeking current research on the utilization of evolving computer vision techniques.
Автор: Mishra Abhishek Название: Machine Learning for IOS Developers ISBN: 1119602874 ISBN-13(EAN): 9781119602873 Издательство: Wiley Рейтинг: Цена: 6018.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner
Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications.
Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book's clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models--both pre-trained and user-built--with Apple's CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers:
Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics
Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming
Develop skills in data acquisition and modeling, classification, and regression.
Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS)
Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML
Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
