Machine Theory

How smart machines are transforming us all - and what we should do about it. The smart-machines revolution is re-shaping our lives and our societies. Here, Nigel Shadbolt, one of Britain's leading authorities on artificial intelligence, and Roger...

In the 1990's it was realized that quantum physics has some spectacular applications in computer science. This book is a concise introduction to quantum computation, developing the basic elements of this new branch of computational theory without...

Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time Series, and Computer Vision. Transformers have gone through many adaptations and alterations,...

This book presents a unified approach to a rich and rapidly evolving research domain at the interface between statistical physics, theoretical computer science/discrete mathematics, and coding/information theory. It is accessible to graduate students and...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9783030123604Author Wolfgang SchererFormat PaperbackPage Count 764Imprint SpringerPublisher SpringerWeight(grams) 155g

How Strategy Works in an Interconnected, Automated WorldLeaders already know that the classic approach to strategy--analyze, plan, execute--is losing relevance. But they don't yet know what replaces it. As everyone and everything becomes more...

The second edition of the second volume in the highly-regarded Write Great Code series, teachinh readers how to produce better machine code by directing the actions of their chosen compiler. This second edition has been updated to cover high-level...

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of...

Foundations of Reinforcement Learning with Applications in Finance aims to demystify Reinforcement Learning, and to make it a practically useful tool for those studying and working in applied areas - especially finance.Reinforcement Learning is emerging...

Deep learning has already achieved remarkable results in many fields. Now it's making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics,...

Organize, plan, and build an exceptional data analytics team within your organization In Minding the Machines: Building and Leading Data Science and Analytics Teams, AI and analytics strategy expert Jeremy Adamson delivers an accessible and insightful...

Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes Key Features Learn the fundamentals of time series analysis and how to model...

With so much artificial intelligence (AI) in the headlines, it is no surprise that businesses are scrambling to exploit this exciting and transformative technology. Clearly, those who are the first to deliver business-relevant AI will gain significant...

Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer...

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features Explore the ins and outs of genetic...

A computational perspective on partial order and lattice theory, focusing on algorithms and their applications This book provides a uniform treatment of the theory and applications of lattice theory. The applications covered include tracking dependency...

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of...

Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman...

It seems that artificial intelligence (AI) is always just five years away, but it never arrives. Recently, however, developments have made the practical utility of game theory a genuine reality. Will sport provide the petri dish in which AI will prove...

Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data. With this book, you'll learn machine...

The study of network theory is a highly interdisciplinary field, which has emerged as a major topic of interest in various disciplines ranging from physics and mathematics, to biology and sociology. This book promotes the diverse nature of the study of...

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithmsKey FeaturesCovers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each...

Text analytics consist of the statistics about a text element, which includes the word count, the word histogram, and the word frequency histogram. Most text documents of value are related to other-sometimes many other-documents, and so analytics...

Master the fundamentals to advanced techniques of causal inference through a practical, hands-on approach with extensive R code examples and real-world applications Key Features Explore causal analysis with hands-on R tutorials and real-world...

Javier Esparza received his primary degree in Theoretical Physics and in 1990 his PhD in Computer Science from the University of Zaragoza. After positions at the University of Hildesheim, the University of Edinburgh, and the Technical University of...

This book offers a gentle motivation and introduction to computational thinking, in particular to algorithms and how they can be coded to solve significant, topical problems from domains such as finance, cryptography, Web search, and data compression. ...

Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methods Purchase of the print or Kindle book...

Learn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architectures Key Features Apply ML and...

An urgent book on generative artificial intelligence exploring the risk and benefits looming in this seminal moment'Easily the best exploration to date on the perils and promise of AI." -ASHLEE VANCE author of When the Heavens Went on Sale'Mastering AI...

The book essentially covers the growing role of AI in the oil and gas industry, including digital technologies used in the exploration phase, customer sales service, and cloud-based digital storage of reservoir simulation data for modeling. It starts...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781632409119Author Margaret NashFormat HardbackPage Count 270Imprint Clanrye InternationalPublisher Clanrye InternationalWeight(grams) 689gDimensions(mm)...

Apologies but we at Booksplease don't have a full description for this book.Book InformationISBN 9781647280284Author Mia WilliamsFormat HardbackPage Count 208Imprint Willford PressPublisher Willford PressWeight(grams) 726gDimensions(mm) 276mm * 203mm *...

The Lanczos Method: Evolution and Application is divided into two distinct parts. The first part reviews the evolution of one of the most widely used numerical techniques in the industry. The development of the method, as it became more robust, is...

Iterative algorithms often rely on approximate evaluation techniques, which may include statistical estimation, computer simulation or functional approximation. This volume presents methods for the study of approximate iterative algorithms, providing...

In this book, we have set up a unified analytical framework for various human-robot systems, which involve peer-peer interactions (either space-sharing or time-sharing) or hierarchical interactions. A methodology in designing the robot behavior through...

Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and Diagnosis discusses various white- and black-box approaches to fault diagnosis in condition monitoring (CM). This indispensable resource:Addresses nearest-neighbor-based,...

The First International Conference on Signal and Information Processing, Networking and Computers (ICSINC) focuses on the key technologies and challenges of signal and information processing schemes, network application, computer theory and application,...

Able to propagate quickly and change their payload with each infection, polymorphic worms have been able to evade even the most advanced intrusion detection systems (IDS). And, because zero-day worms require only seconds to launch flooding attacks on...

This book covers the different technologies of Internet, and machine learning capabilities involved in Cognitive Internet of Things (CIoT). Machine learning is explored by covering all the technical issues and various models used for data analytics...

In response to scientific needs for more diverse and structured explanations of statistical data, researchers have discovered how to model individual data points as belonging to multiple groups. Handbook of Mixed Membership Models and Their Applications...