Artificial intelligence (AI) Books
Taylor & Francis Ltd AI for Death and Dying
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£114.00
Taylor & Francis Ltd Robotics Autonomous Systems and Contemporary International Security
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£128.25
Taylor & Francis Ltd Robotics Autonomous Systems and Contemporary International Security
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£39.99
Taylor & Francis Ltd AI for Radiology
Book SynopsisArtificial Intelligence has revolutionised areas of medicine. This book focuses on the integral role of AI in radiology, shedding light on how this technology can enhance patient care and streamline professional workflows.Trade Review“The book is not just about the present state of affairs. It offers a vision, exploring the future trajectories of AI in radiology, addressing challenges, controversies, and the endless possibilities on the horizon.Having witnessed Oge’s dedication and forward-thinking approach firsthand, I am confident that this book will serve as an invaluable resource. For those stepping into the realm of AI in radiology or seeking to deepen their knowledge, this book provides a holistic, scientifically rigorous, and practical guide…I wholeheartedly believe that it will stand as a cornerstone for all enthusiasts eager to delve into the world of AI in Radiology.”--Felipe Kitamura, MD, PhDDirector of Applied Innovation and AI at DasaAffiliated Professor of Radiology at Universidade Federal de São PauloTable of Contents1 Artificial Intelligence and Medicine: The Big Picture2 AI in Radiology: From Fear to Leadership3 Fundamentals of Machine Learning and Deep Learning4 Fundamentals of Medical Image Analysis5 Data: The Essential Ingredient in AI Solutions6 Clinical Applications of AI in Radiology7 Harnessing AI in Radiology Education and Training8 Getting Started with Deep Learning in Medical Imaging9 The Future of AI in Radiology10 Resources for Further Learning
£22.99
Taylor & Francis Strategic Communication and AI
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£47.49
Taylor & Francis Ltd Machine Vision for Industry 4.0
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£104.50
Taylor & Francis Ltd The Future of the Artificial Mind
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£256.50
Taylor & Francis Ltd The Future of the Artificial Mind
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£104.50
Taylor & Francis Artificial Intelligence Automation and the Future of Competence at Work Routledge Studies in the Economics of Innovation
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£109.25
Taylor & Francis Ltd Artificial Intelligence Automation and the Future of Competence at Work
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£39.99
Taylor & Francis Ltd AI for Games
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£114.00
Taylor & Francis Ltd Artificial Intelligence and Machine Learning in Business Management
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£104.50
Taylor & Francis Ltd Handbook of Sustainable Development Through Green Engineering and Technology
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£156.75
Taylor & Francis Ltd Intelligent Virtual System for Underwater Vehicle Piloting using UnityTM
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£80.74
Taylor & Francis Communicating Artificial Intelligence AI Theory Research and Practice
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£128.25
Taylor & Francis Ltd Communicating Artificial Intelligence AI
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£37.99
Taylor & Francis Ltd Computational Intelligence for Information Retrieval
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£104.50
Taylor & Francis Ltd Regulating Artificial Intelligence
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£37.99
Taylor & Francis Ltd Blockchain for Healthcare Systems
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£104.50
Taylor & Francis Ltd Explanatory Model Analysis
Book SynopsisExplanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model''s performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.Trade Review"The structure is well-conceived, with chapters consisting in five sections: intuition, method, example, pros and cons, and code snippets. I sense a teacher’s long experience behind these choices.The chapters contain good mathematical detail on the techniques discussed, but the theory is well balanced with examples and code.The visualizations are great. Often, the gist of a particular technique, and it’s practical, interpretive value, can be gleaned from the visualizations threading through the chapter, along with captions. The authors did a really nice job with this.The rationale for the book is well-described.The discussion of techniques seems both comprehensive (given my sense of the field) and helpfully specific, both at the instance and the dataset levels."-Jeff Webb, University of Utah"The authors are doing a very good job in addressing the potential readers, by providing a clean presentation and practical guidance on diagnostic graphical tools…Having an ‘intuition section’ at the beginning of each chapter is very useful."-Riccardo De Bin, University of Oslo"The book provides a unified presentation of model exploration, visualization, comparison and diagnostics of different machine learning algorithms…This book would be found useful by both students as well as practitioners who analyze their own data. Books including real data examples in R and in Python are needed in this area. (It) will serve as a reference, especially for analyses done with dalex or archivist R package (and )can serve as a textbook of data science courses in many fields including computer science, social sciences, economics and other."-Patricia Martinkova, Institute of Computer Science of the Czech Academy of Sciences"There are books that focus on prediction models, for example the element of statistical learning and an introduction to statistical learning but these are not focused on the evaluation of predictive models which is the main focus on the proposed book and its main advantage. As predictive models become very popular in the last years, such a book that focus on the evaluation of the models and model diagnostics can be very popular."-Ziv Shkedy, Data Science Institute, Hasselt University, Belgium'The book is clearly and consistently structured and well–written. The graphics are explained conceptually and mathematically. There are chapter sections on the pros and cons of what is proposed, where the authors are generally properly cautious and recommend a mixture of approaches.'- Antony Unwin, International Statistical Review, 2021 Volume 89, Issue 3"The structure is well-conceived, with chapters consisting in five sections: intuition, method, example, pros and cons, and code snippets. I sense a teacher’s long experience behind these choices. The chapters contain good mathematical detail on the techniques discussed, but the theory is well balanced with examples and code. The visualizations are great. Often, the gist of a particular technique, and it’s practical, interpretive value, can be gleaned from the visualizations threading through the chapter, along with captions. The authors did a really nice job with this. The rationale for the book is well-described. The discussion of techniques seems both comprehensive (given my sense of the field) and helpfully specific, both at the instance and the dataset levels." -Jeff Webb, University of Utah"The authors are doing a very good job in addressing the potential readers, by providing a clean presentation and practical guidance on diagnostic graphical tools…Having an ‘intuition section’ at the beginning of each chapter is very useful." -Riccardo De Bin, University of Oslo"The book provides a unified presentation of model exploration, visualization, comparison and diagnostics of different machine learning algorithms…This book would be found useful by both students as well as practitioners who analyze their own data. Books including real data examples in R and in Python are needed in this area. (It) will serve as a reference, especially for analyses done with dalex or archivist R package (and )can serve as a textbook of data science courses in many fields including computer science, social sciences, economics and other." -Patricia Martinkova, Institute of Computer Science of the Czech Academy of Sciences"There are books that focus on prediction models, for example the element of statistical learning and an introduction to statistical learning but these are not focused on the evaluation of predictive models which is the main focus on the proposed book and its main advantage. As predictive models become very popular in the last years, such a book that focus on the evaluation of the models and model diagnostics can be very popular." -Ziv Shkedy, Data Science Institute, Hasselt University, Belgium"We need to explore the models and learn about their behaviour. This book presents, explains, and summarises the techniques for doing so. Moreover, it provides code in R and Python for doing so. The methods have many similarities with those of sensitivity analysis developed within the Sensitivity Analysis of Model Output (SAMO) community. ... [M]any doctoral students, professional statisticians and researchers should ensure that they have access to it and know how to use its methods when dealing with highly complex functions in their data and model analysis." -Simon French, in the Journal of the Royal Statistics Society, Series A, June 2022"The book presents a valuable collection of methods for models’ exploration and diagnostics for various machine learning algorithms. It can be useful in the data and computer science courses for students and instructors, as well as for researchers and practitioners who need to analyze and interpret their statistical and machine learning models both of glass-box and blackbox kind. The book also serves as a great primary for applications of the R and Python software and their packages/libraries, so it is valuable in solving various problems of statistical prediction in various fields."-Stan Lipovetsky, in Technometrics, July 2022Table of ContentsI. Introduction 1. Introduction. 2. Model Development. 3. Do-it-yourself. 4. Datasets and models. II. Instance Level. 5. Introduction to Instance-level Exploration. 6. Break-down Plots for Additive Attributions. 7. Break-down Plots for Interactions. 8. Shapley Additive Explanations (SHAP) for Average Attributions. 9. Local Interpretable Model-agnostic Explanations (LIME). 10. Ceteris-paribus Profiles. 11. Ceteris-paribus Oscillations. 12. Local-diagnostics Plots. 13. Summary of Instance-level Exploration. III. Dataset Level. 14. Introduction to Dataset-level Exploration. 15. Model-performance Measures. 16. Variable-importance Measures. 17. Partial-dependence Profiles. 18. Local-dependence and Accumulated-dependence Profiles. 19. Residual Diagnostics Plots. 20. Summary of Model-level Exploration. IV. Use-cases. 21. FIFA 19.
£45.99
Taylor & Francis The Algorithmic Unconscious How Psychoanalysis Helps in Understanding AI Antinomies
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£128.25
Taylor & Francis Ltd The Algorithmic Unconscious
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£39.99
Taylor & Francis Ltd How Things Work
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£43.69
Taylor & Francis Ltd DataDriven ModelFree Controllers
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£128.25
Taylor & Francis Ltd How Things Work
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£114.00
Taylor & Francis Ltd The Economics of Digital Transformation
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£128.25
Taylor & Francis Ltd Reinventing Manufacturing and Business Processes Through Artificial Intelligence
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£128.25
Taylor & Francis Ltd Smart Sensor Networks Using AI for Industry 4.0
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£104.50
Taylor & Francis Ltd Applied Edge AI
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£94.99
Taylor & Francis Machine Learning and AI in Finance
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£128.25
Taylor & Francis Ltd Machine Learning and AI in Finance
Book SynopsisThe significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables. The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stockTable of ContentsForewordMarcos Lopez de PradoIntroductionGermán G. Creamer, Gary Kazantsev and Tomaso Aste1. Universal features of price formation in financial markets: perspectives from deep learningJustin Sirignano and Rama Cont2. Far from the madding crowd: collective wisdom in prediction marketsGiulio Bottazzi and Daniele Giachini3. Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamicsYing Chen, Wee Song Chua and Wolfgang Karl Härdle4. Forecasting market statesPier Francesco Procacci and Tomaso Aste5. Encoding of high-frequency order information and prediction of short-term stock price by deep learningDaigo Tashiro, Hiroyasu Matsushima, Kiyoshi Izumi and Hiroki Sakaji6. Attention mechanism in the prediction of stock price movement by using LSTM: Evidence from the Hong Kong stock market Shun Chen and Lei Ge7. Learning multi-market microstructure from order book dataGeonhwan Ju, Kyoung-Kuk Kim and Dong-Young Lim8. A non-linear causality test: a machine learning approach for energy futures forecastGermán G. Creamer and Chihoon Lee9. The QLBS Q-Learner goes NuQLear: fitted Q iteration, inverse RL, and option portfoliosIgor Halperin10. Detection of false investment strategies using unsupervised learning methodsMarcos López de Prado and Michael J. Lewis
£39.99
Taylor & Francis The Future of Digital Data Heritage and Curation in a MorethanHuman World
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£128.25
Taylor & Francis Ltd Fieldbus and Networking in Process Automation
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£137.75
Taylor & Francis Ltd Introduction to Lattice Algebra
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£99.75
Taylor & Francis Expert Systems in Finance
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£39.99
Taylor & Francis Ltd AI vs Humans
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£128.25
Taylor & Francis Ltd Bayesian Reasoning and Gaussian Processes for Machine Learning Applications
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£104.50
Taylor & Francis Ltd The Routledge Handbook of FinTech
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£39.99
Taylor & Francis Ltd Artificial IntelligenceAided Materials Design
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£104.50
Taylor & Francis Ltd Biodesign in the Age of Artificial Intelligence
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£128.25
Taylor & Francis Ltd Physics of Data Science and Machine Learning
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£80.74
Taylor & Francis Ltd Everyday Automation
Book SynopsisThis Open Access book brings the experiences of automation as part of quotidian life into focus. It asks how, where and when automated technologies and systems are emerging in everyday life across different global regions? What are their likely impacts in the present and future? How do engineers, policy makers, industry stakeholders and designers envisage artificial intelligence (AI) and automated decision-making (ADM) as solutions to individual and societal problems? How do these future visions compare with the everyday realities, power relations and social inequalities in which AI and ADM are experienced? What do people know about automation and what are their experiences of engaging with actually existing' AI and ADM technologies? An international team of leading scholars bring together research developed across anthropology, sociology, media and communication studies and ethnology, which shows how by rehumanising automation, we can gain deeper understandings of its societalTable of ContentsIntroduction: Everyday Automation: setting a research agenda PART I: Challenging dominant narratives of automation 1. Imagining Mundane Automation: Historical Trajectories of Meaning Making around Technological Change 2. Trust, Ethics and Automation: Anticipatory Imaginaries in Everyday Life 3. The Quantified Pandemic: Digitised Surveillance, Containment and Care in Response to the COVID-19 Crisis 4. Less Work for Teacher? The Ironies of Automated Decision-making in Schools PART II Embedding automated systems in the everyday 5. Alexa’s Got a Hunch: The Human Decisions behind Programming Emotion-sensing and Caregiving into Digital Assistants 6. Framing Fashion: Human-Machine Learning and the Amazon Echo Look 7. Coffee with the Algorithm: Imaginaries, Maintenance and Care in the Everyday Life of a News-ranking Algorithm 8. Everyday AI at Work: Self-tracking and Automated Communication for Smart Work 9. Exploring ADM in Clinical Decision-Making: Healthcare Experts Encountering Digital Automation PART III Experimenting with Automation in Society 10. Hate it? Automate it!: Thinking and Doing Robotic Process Automation and Beyond 11. Smart Thermostats and the Algorithmic Control of Thermal Comfort 12. Prisoners Training AI: Ghosts, Humans and Values in Data Labour 13. Investigating ADM in Shared Mobility: A Design Ethnographic Approach 14. Ad Accountability Online: A Methodological Approach.
£36.99
Taylor & Francis Ltd Everyday Automation
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£128.25
Taylor & Francis Ltd Regulating Artificial Intelligence in Industry
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£128.25
Taylor & Francis Ltd Artificial Intelligence in Commercial Aviation
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£128.25
Taylor & Francis Ltd Cyber Arms
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£68.39
Taylor & Francis AgentBased Computational Economics
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£147.25
Taylor & Francis Handbook of Automated Essay Evaluation Current Applications and New Directions By Mark D Shermis published May 2013
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£114.00