Machine Theory

AI is nothing without somewhere to run it. Now that mobile devices have become the primary computing device for most people, it's essential that mobile developers add AI to their toolbox. This insightful book is your guide to creating models and running...

This compendium brings together the fields of Quantum Computing, Machine Learning, and Neuromorphic Computing. It provides an elementary introduction for students and researchers interested in quantum or neuromorphic computing to the basics of machine...

This textbook offers a unified and self-contained introduction to the field of term rewriting. It covers all the basic material (abstract reduction systems, termination, confluence, completion, and combination problems), but also some important and...

The most powerful weapon in business today is the alliance between the mathematical smarts of machines and the imaginative human intellect of great leaders. Together they make the mathematical corporation, the business model of the future.We are at a...

Despite the range of studies into grief and mourning in relation to the digital, research to date largely focuses on the cultural practices and meanings that are played out in and through digital environments. Digital Afterlife brings together experts...

This is an open access title available under the terms of a CC BY-NC-ND 4.0 International license. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. Computing systems are...

The goal of Optimal Transport (OT) is to define geometric tools that are useful to compare probability distributions. Their use dates back to 1781.Recent years have witnessed a new revolution in the spread of OT, thanks to the emergence of approximate...

Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. The model-based framework allows the problems of choosing or developing an appropriate...

Introduction to Machine Learning with Applications in Information Security provides a class-tested introduction to a wide variety of machine learning algorithms, reinforced through realistic applications. The book is accessible and doesn't prove...

This textbook is a comprehensive introduction to computational mathematics and scientific computing suitable for undergraduate and postgraduate courses. It presents both practical and theoretical aspects of the subject, as well as advantages and pitfalls...

In the midst of the fourth industrial revolution, big data is weighed in gold, placing enormous power in the hands of data scientists - the modern AI alchemists. But great power comes with greater responsibility. This book seeks to shape, in a practical,...

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from...

Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for creating...

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...

A unified view of metaheuristics This book provides a complete background on metaheuristics and shows readers how to design and implement efficient algorithms to solve complex optimization problems across a diverse range of applications, from networking...

Physical and behavioral biometric technologies such as fingerprinting, facial recognition, voice identification, etc. have enhanced the level of security substantially in recent years. Governments and corporates have employed these technologies to...

"Automated scoring engines [...] require a careful balancing of the contributions of technology, NLP, psychometrics, artificial intelligence, and the learning sciences. The present handbook is evidence that the theories, methodologies, and underlying...

In this monograph, the authors present an introduction to the framework of variational autoencoders (VAEs) that provides a principled method for jointly learning deep latent-variable models and corresponding inference models using stochastic gradient...

Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third...

Are you familiar with the IEEE floating point arithmetic standard? Would you like to understand it better? This book gives a broad overview of numerical computing, in a historical context, with special focus on the IEEE standard for binary floating point...

This book focuses on the application of soft computing in materials and manufacturing sectors with the objective to offer an intelligent approach to improve the manufacturing process, material selection and characterization techniques for developing...

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Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks...

Implementing new information technologies into the healthcare sector can provide alternatives to managing patients’ health records, systems, and improving the quality of care received. This book provides an overview of Internet of Things (IoT)...

Computational science is a rapidly growing multidisciplinary field concerned with the design, implementation, and use of mathematical models to analyze and solve real-world problems. It is an area of science that spans many disciplines and which involves...

Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services, real estate business, consumer goods,...

Machine learning approaches has the capability to learn and adapt to the constantly evolving demands of large Internet-of-energy (IoE) network. The focus of this book is on using the machine learning approaches to present various solutions for IoE...

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Computational Intelligence Techniques and Their Applications to Software Engineering Problems focuses on computational intelligence approaches as applicable in varied areas of software engineering such as software requirement prioritization, cost...

A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or...

The purpose of this edited book is to provide the relevant technologies and case studies in a concise format that will simplify and streamline the processing of blockchain. The goal is for the contents of this book to change the way business...

This book introduces the principle of carrying out a medium-term load forecast (MTLF) at power system level, based on the Big Data concept and Convolutionary Neural Network (CNNs). It also presents further research directions in the field of Deep...

The Oxford Topology Symposium was held in June 1989. Since techniques from topology and category theory have been used increasingly by theoretical computer scientists in recent years, it was decided to hold a special session at the symposium which would...

Logicians have developed beautiful algorithmic techniques for the construction of computably enumerable sets. This textbook presents these techniques in a unified way that should appeal to computer scientists. Specifically, the book explains, organizes,...

Spin glass models were introduced by physicists in the 1970s to model the statistical properties of certain magnetic materials. Over the last half century, these models have motivated a blossoming line of mathematical work with applications to multiple...

Probably almost correct (PAC) bounds have been an intensive field of research over the last two decades. Hundreds of papers have been published and much progress has been made resulting in PAC-Bayes bounds becoming an important technique in machine...

Reinforcement learning agents have demonstrated remarkable achievements in simulated environments. Data efficiency, however, significantly impedes carrying this success over to real environments. The design of data-efficient agents that address this...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781638282082Author Brandon AmosFormat PaperbackPage Count 156Imprint now publishers IncPublisher now publishers IncWeight(grams) 230g

Deep learning (DL) has proven to be a highly effective approach for developing models in diverse contexts, including visual perception, speech recognition, and machine translation. Automated deep learning (AutoDL) endeavors to minimize the need for human...

This book is designed as a reference text and provides a comprehensive overview of conceptual and practical knowledge about deep learning in medical image processing techniques. The post-pandemic situation teaches us the importance of doctors, medical...