Computer science Books
Taylor & Francis Ltd Artificial Intelligence and Machine Learning in 2D3D Medical Image Processing
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£45.99
Taylor & Francis Ltd Cyber Crime Investigators Field Guide
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£54.14
Taylor & Francis Ltd A Step Towards Society 5.0
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£128.25
Taylor & Francis Ltd Hidden and Devalued Feminized Labour in the Digital Humanities
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£118.75
Taylor & Francis Ltd Hidden and Devalued Feminized Labour in the Digital Humanities
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£35.99
Taylor & Francis Ltd An Introduction to IoT Analytics
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£44.99
Taylor & Francis Ltd An Introduction to IoT Analytics
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£109.25
Taylor & Francis Ltd Advanced Smart Computing Technologies in Cybersecurity and Forensics
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£45.99
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 Ltd Smart Sensor Networks Using AI for Industry 4.0
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£104.50
Taylor & Francis Ltd Nanoelectronics for NextGeneration Integrated Circuits
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£118.75
Taylor & Francis Ltd Human Rights and the Digital Divide
Book SynopsisThe Internetâs importance for freedom of expression and other rights comes in part from the ability it bestows on users to create and share information, rather than just receive it. Within the context of existing freedom of expression guarantees, this book critically evaluates the goal of bridging the 'digital divide' â the gap between those who have access to the Internet and those who do not. Central to this analysis is the examination of two questions: first, is there a right to access the Internet, and if so, what does that right look like and how far does it extend? Second, if there is a right to access the Internet, is there a legal obligation on States to overcome the digital divide?Through examination of this debateâs history, analysis of case law in the European Court of Human Rights and Inter-American Court of Human Rights, and a case study of one digital inclusion programme in Jalisco, Mexico, this book concludes that there is indeed currently a legal right to IntTable of Contents1. General Introduction 2. Historical Context of International Debates on a Right to Access to Information Technology and Digital Divide Guarantees3. Negative Obligations 4. Positive Obligations to Facilitate Access to the Internet5. Looking Outside Freedom of Expression to Strengthen Digital Inclusion Guarantees6. Case Study in Digital Inclusion7. Conclusions
£41.99
Taylor & Francis Ltd The Routledge Handbook of the Computational Mind
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£43.99
Taylor & Francis Ltd Fundamentals of Image Audio and Video Processing Using MATLAB
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£42.99
Taylor & Francis Ltd Situating Data Science
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£37.04
Taylor & Francis Ltd Physics of Data Science and Machine Learning
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£80.74
Taylor & Francis Ltd Implementing Digital Forensic Readiness From Reactive to Proactive Process Second Edition
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£65.54
Taylor & Francis Ltd Computational Biology
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£42.74
Taylor & Francis Information and Communication Technologies for Development Evaluation
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£39.99
Taylor & Francis Ltd Creating Value with Data Analytics in Marketing
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£147.25
Taylor & Francis Ltd Green Internet of Things for Smart Cities
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£142.50
Taylor & Francis Ltd Federated AI for RealWorld Business Scenarios
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£137.75
Taylor & Francis Free Software the Internet and Global Communities of Resistance
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£38.99
Taylor & Francis Digital Music Distribution
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£41.99
Taylor & Francis The Diffusion of Information and Communication Technologies
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£39.99
Taylor & Francis The Human Factor in Machine Translation
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£41.99
Taylor & Francis Ltd Fundamentals of Image Audio and Video Processing Using MATLAB
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£105.00
Taylor & Francis Ltd Management in the Era of Big Data
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£94.99
Taylor & Francis Ltd Data Analytics and AI
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£50.34
Taylor & Francis Ltd Quantitative Methodologies using MultiMethods
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£118.75
Taylor & Francis Ltd Applications in Computing for Social Anthropologists
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£24.51
Taylor & Francis Ltd Geographic Information Systems Socioeconomic Applications
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£51.29
Taylor & Francis Ltd An Introduction to Longitudinal Research Social Research Today
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£45.59
Taylor & Francis Ltd Distributed Learning Social and Cultural Approaches to Practice
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£49.39
Taylor & Francis Distributed Learning Social and Cultural Approaches to Practice
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£22.99
Taylor & Francis Ltd Expressive Form
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£45.59
Taylor & Francis Ltd Electronic Democracy
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£128.25
Taylor & Francis Ltd Next Generation Geospatial Information From Digital Image Analysis to Spatiotemporal Databases ISPRS Book Series
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£118.75
Taylor & Francis Ltd Social and Community Informatics
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£51.29
Taylor & Francis Interpretation in Architecture
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£137.75
Taylor & Francis Rethinking Information Systems in Organizations
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£175.75
Taylor & Francis Rethinking Information Systems in Organizations
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£65.54
Taylor & Francis Managing Information and Knowledge in Organizations
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£175.75
Taylor & Francis Managing Information and Knowledge in Organizations
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£56.04
Taylor & Francis Understanding EGovernment
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£171.00
Taylor & Francis Understanding EGovernment
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£47.49
Taylor & Francis MATLAB for Behavioral Scientists
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£171.00
Taylor & Francis MATLAB for Behavioral Scientists
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£52.24