Machine learning Books
Taylor & Francis Ltd Machine Learning in Signal Processing
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£999.99
Taylor & Francis Ltd Machine Learning for Factor Investing
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£999.99
Taylor & Francis Ltd Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics
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£999.99
Taylor & Francis Ltd Machine Learning for Computer and Cyber Security Principle Algorithms and Practices
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£999.99
Taylor & Francis Ltd Deep and Shallow
Book SynopsisProviding an essential and unique bridge between the theories of signal processing, machine learning, and artificial intelligence (AI) in music, this book provides a holistic overview of foundational ideas in music, from the physical and mathematical properties of sound to symbolic representations. Combining signals and language models in one place, this book explores how sound may be represented and manipulated by computer systems, and how our devices may come to recognize particular sonic patterns as musically meaningful or creative through the lens of information theory.Introducing popular fundamental ideas in AI at a comfortable pace, more complex discussions around implementations and implications in musical creativity are gradually incorporated as the book progresses. Each chapter is accompanied by guided programming activities designed to familiarize readers with practical implications of discussed theory, without the frustrations of free-form coding.Surveying sTrade Review"Deep and Shallow by Shlomo Dubnov and Ross Greer is an exceptional journey into the convergence of music, artificial intelligence, and signal processing. Seamlessly weaving together intricate theories with practical programming activities, the book guides readers, whether novices or experts, toward a profound understanding of how AI can reshape musical creativity. A true gem for both enthusiasts and professionals, this book eloquently bridges the gap between foundational concepts of music information dynamics as an underlying basis for understanding music structure and listening experience, and cutting-edge applications, ushering us into the future of music and AI with clarity and excitement."Gil Weinberg, Professor and Founding Director, Georgia Tech Center for Music Technology"The authors make an enormous contribution, not only as a textbook, but as essential reading on music information dynamics, bridging multiple disciplines of music, information theory, and machine learning. The theory is illustrated and grounded in plenty of practical information and resources."Roger B. Dannenberg, Emeritus Professor of Computer Science, Art & Music, Carnegie Mellon UniversityTable of ContentsPrefaceChapter 1 Introduction to Sounds of MusicChapter 2 Noise: the Hidden Dynamics of MusicChapter 3 Communicating Musical InformationChapter 4 Understanding and (Re)Creating Sound Chapter 5 Generating and Listening to Audio InformationChapter 6 Artificial Musical BrainsChapter 7 Representing Voices in Pitch and TimeChapter 8 Noise Revisited: Brains that ImagineChapter 9 Paying (Musical) AttentionChapter 10 Last Noisy Thoughts, Summary and ConclusionAppendix A Introduction to Neural Network Frameworks: Keras, Tensorflow, PytorchAppendix B Summary of Programming Examples and ExercisesAppendix C Software Packages for Music and Audio Representation and AnalysisAppendix D Free Music and Audio Editting SoftwareAppendix E DatasetsAppendix F Figure AttributionsReferences Index
£999.99
Taylor & Francis Ltd Machine Learning
Book SynopsisMachine Learning: Concepts, Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases, self-assessments, exercises, activities, numerical problems, and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison Table of Contents1. Introduction. 2. Understanding Machine Learning. 3. Mathematiccal Foundations and Machine Learning. 4. Foundations and categoris of Machine Learning Techniques. 5. Machine Learning: Tool and Software 6. Classification Algorithms. 7. Probabilistic and Regression based approaches. 8. Performance Evaluation & Ensemble Methods. 9. Unsupervised Learning. 10. Sequence Models. 11. Reinforcement Learning. 12. Machine Learning Applications – Approaches. 13. Domain based Machine Learning Applications. 14. Ethical Aspects of Machine Learning. 15. Introduction to Deep Learning and Convolutional Neural Networks. 16. Other Models of Deep Learning and Applications of Deep Learning.
£999.99
Taylor & Francis Ltd Machine Learning for Managers
Book SynopsisMachine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations.Trade Review"If you are considering implementing machine learning in your business but don’t know where to start, this is the right book for you. Machine Learning for Managers is a comprehensive but non-technical introduction to the topic with many relevant examples and implementation guidelines. The split into a detailed overview and project management instructions is ideal for readers who don’t have the time to acquire programming skills but are passionate about leveraging AI to enhance business performance. The author’s very engaging writing style makes reading a book about a potentially very dry topic enjoyable."Christoph Schumacher, Professor of Innovation and Economics; Director Knowledge Exchange Hub, Massey University, New Zealand"This book fills an important gap between pure-technical and pure-managerial descriptions of machine learning (ML). Written in a no-nonsense light-hearted style, it is easy to follow, yet doesn’t shy away from using technical terms that are important for managers to be able to speak to their ML engineers. Highly recommended for managers looking to understand more about what is under the hood of ML."Tava Olsen, Professor, Deputy Dean, Melbourne Business School"Machine Learning for Managers is a safe haven for non-technical readers interested in understanding what AI and specifically ML is about. With clear, direct and witty language, Geertsema ensures that our journey into AI is like a walk in the park. It is easy, pleasurable and refreshing in its approach and powerful in its choice of illustrations. It brings to the forefront key concepts such as explainability, governance and business case making the message lucent and highly applicable to managers interested in incorporating ML into their business. As a practitioner focussed on human centric AI, I am particularly keen in bringing down AI/ML from its ivory tower status. This book is exactly a tool for this as it provides transparency, deciphers otherwise perceived complex language and is the basis for what ML should do best: to serve you. By far the best introductory ML roadmap I have come across. A must read."Jose Romano, Senior Manager at the European Investment Fund and former Entrepreneur in Residence at TAZI.AI"The two complementary parts of the book form a comprehensive and practical guide to machine learning. The first part provides a nontechnical overview of machine learning algorithms, demystifying the jargon in the field, which is crucial for students, lecturers and practitioners aiming to apply machine learning to resolve real-life business problems. The second part insightfully examines how machine learning outcomes can be developed and deployed in the organisation's processes. A recommended work for anyone looking to successfully manage the tsunami of big data!"Leo Paas, Professor, The University of Auckland Business School; Program Director, Master of Business Analytics"This book provides an outstanding introduction to machine learning from a management perspective. It gives a very clear presentation of the state-of-the-art machine learning methods and how to manage machine learning projects efficiently. It brings a fresh, unique focus on how to learn machine learning from a business perspective. It is highly practical and discusses in detail how a machine learning project should be deployed in real business applications. Not to be missed by any manager with a serious interest in AI and Machine Learning."Albert Bifet, Professor, Director of the AI Institute, The University of Waikato, New ZealandTable of ContentsPart 1: Understanding Machine Learning 1. Let's jump right in 2. Different kinds of ML 3. Creating ML models 4. Linear models 5. Neural networks 6. Tree-based approaches, ensembles and boosting 7. Dimensionality reduction and clustering 8. Unstructured data 9. Explainable AI Part 2: Managing Machine Learning Projects 10. The ML system lifecycle 11. The big picture 12. Creating value with ML 13. Making the business case 14. The ML pipeline 15. Development 16. Deployment and monitoring
£999.99
Taylor & Francis Ltd Deep Learning
Book SynopsisThis book focuses on deep learning (DL), which is an important aspect of data science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising. The design of the DL models based on artificial neural networks is influenced by the structure and operation of the brain. This book presents a comprehensive resource for those who seek a solid grasp of the techniques in DL.Key features: Provides knowledge on theory and design of state-of-the-art deep learning models for real-world applications Explains the concepts and terminology in problem-solving with deep learning Explores the theoretical basis for major algorithms and approaches in deep learning Discusses the enhancement techniques of deep learning models Identifies the performance evaluation techniques for deep learning models Accordingly, the book covers the entire process flowTable of Contents1. Introduction. 2. Concepts and Terminology. 3. State-of-the-Art Deep Learning Models: Part I. 4. State-of-the-Art Deep Learning Models: Part II. 5. Advanced Learning Techniques. 6. Enhancement of Deep Learning Architectures. 7. Performance Evaluation Techniques.
£999.99
Taylor & Francis Ltd Feature Engineering for Machine Learning and Data
Book SynopsisFeature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specifTable of Contents1. Preliminaries and Overview 2. Feature Engineering for Text Data 3. Feature Extraction and Learning for Visual Data 4. Feature-based time-series analysis 5. Feature Engineering for Data Streams 6. Feature Generation and Feature Engineering for Sequences 7. Feature Generation for Graphs and Networks 8. Feature Selection and Evaluation 9. Automating Feature Engineering in Supervised Learning 10. Pattern based Feature Generation 11. Deep Learning for Feature Representation 12. Feature Engineering for Social Bot Detection 13. Feature Generation and Engineering for Software Analytics 14. Feature Engineering for Twitter-based Applications
£999.99
Elsevier Science & Technology Artificial Intelligence: A New Synthesis
Book SynopsisIntelligent agents are employed as the central characters in this introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. A distinguishing feature of this text is in its evolutionary approach to the study of AI. This book provides a refreshing and motivating synthesis of the field by one of AI's master expositors and leading researches.Table of ContentsReactive Machines. Neural Networks. Machine Evolution. State Machines. Robot Vision. Search in State Spaces. Agents that Plan. Uninformed Search. Heuristic Search. Planning, Acting and Learning. Alternative Search. Knowledge Representation and Reasoning. The Propositional Calculus. The Predicate Calculus. Knowledge-based Systems. Representing Common sense Knowledge. Reasoning with Uncertain Information. Learning and Acting with Bayes Nets. Planning Methods Based on Logic. The Situation Calculus. Planning. Communication and Integration. Multiple Agents. Communication Among Agents. Agent Architectures.
£54.89
Cambridge University Press OnLine Learning in Neural Networks
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£42.99
Cambridge University Press Neural Network Learning Theoretical Foundations
Book SynopsisThis book describes theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the VapnikâChervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the VapnikâChervonenkis dimension in large margin classification, and in real prediction. They also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient constructive learning algorithms. The book is self-contained and is intended to be accessible to researchers and graduaTrade Review'The book is a useful and readable mongraph. For beginners it is a nice introduction to the subject, for experts a valuable reference.' Zentralblatt MATHTable of Contents1. Introduction; Part I. Pattern Recognition with Binary-output Neural Networks: 2. The pattern recognition problem; 3. The growth function and VC-dimension; 4. General upper bounds on sample complexity; 5. General lower bounds; 6. The VC-dimension of linear threshold networks; 7. Bounding the VC-dimension using geometric techniques; 8. VC-dimension bounds for neural networks; Part II. Pattern Recognition with Real-output Neural Networks: 9. Classification with real values; 10. Covering numbers and uniform convergence; 11. The pseudo-dimension and fat-shattering dimension; 12. Bounding covering numbers with dimensions; 13. The sample complexity of classification learning; 14. The dimensions of neural networks; 15. Model selection; Part III. Learning Real-Valued Functions: 16. Learning classes of real functions; 17. Uniform convergence results for real function classes; 18. Bounding covering numbers; 19. The sample complexity of learning function classes; 20. Convex classes; 21. Other learning problems; Part IV. Algorithmics: 22. Efficient learning; 23. Learning as optimisation; 24. The Boolean perceptron; 25. Hardness results for feed-forward networks; 26. Constructive learning algorithms for two-layered networks.
£56.16
Cambridge University Press Relational Knowledge Discovery
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£64.99
Cambridge University Press Evaluating Learning Algorithms
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£127.00
Cambridge University Press Neural Network Learning Theoretical Foundations
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£114.00
Cambridge University Press Computational Learning Theory 30 Cambridge Tracts in Theoretical Computer Science Series Number 30
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£49.23
Cambridge University Press OnLine Learning in Neural Networks 17 Publications of the Newton Institute Series Number 17
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£133.00
Cambridge University Press Grammatical Inference
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£93.99
Cambridge University Press Statistical Mechanics of Learning
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£120.00
Cambridge University Press Statistical Mechanics of Learning
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£62.99
Cambridge University Press The Text Mining Handbook
Book SynopsisPresents a comprehensive discussion of the state-of-the-art in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world, mission-critical applications.Trade Review' … buy the book. This book is definitely worth having in your book shelf as a handy reference.' IAPR NewsletterTable of Contents1. Introduction to text mining; 2. Core text mining operations; 3. Text mining preprocessing techniques; 4. Categorization; 5. Clustering; 6. Information extraction; 7. Probabilistic models for Information extraction; 8. Preprocessing applications using probabilistic and hybrid approaches; 9. Presentation-layer considerations for browsing and query refinement; 10. Visualization approaches; 11. Link analysis; 12. Text mining applications; Appendix; Bibliography.
£74.99
Cambridge University Press Algebraic Geometry and Statistical Learning Theory 25 Cambridge Monographs on Applied and Computational Mathematics Series Number 25
Book SynopsisSure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning machines applied to information science have a parameter space that is singular: mixture models, neural networks, HMMs, Bayesian networks, and stochastic context-free grammars are major examples. Algebraic geometry and singularity theory provide the necessary tools for studying such non-smooth models. Four main formulas are established: 1. the log likelihood function can be given a common standard form using resolution of singularities, even applied to more complex models; 2. the asymptotic behaviour of the marginal likelihood or 'the evidence' is derived based on zeta function theory; 3. new methods are derived to estimate the generalization errors in Bayes and Gibbs estimations from training errors; 4. the generalization errors of maximum likelihood and a posteriori methods are clarified by empirical process theory oTrade Review"Overall, the many insightful remarks and simple direct language make the book a pleasure to read." Shaowei Lin, Mathematical ReviewsTable of ContentsPreface; 1. Introduction; 2. Singularity theory; 3. Algebraic geometry; 4. Zeta functions and singular integral; 5. Empirical processes; 6. Singular learning theory; 7. Singular learning machines; 8. Singular information science; Bibliography; Index.
£76.99
Cambridge University Press Mathematical Pictures at a Data Science Exhibition
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£43.30
Cambridge University Press Deep Learning for Natural Language Processing
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£29.99
Cambridge University Press Strategizing AI in Business and Education
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£21.54
Cambridge University Press Intelligent Metasurface Sensors
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£21.54
Cambridge University Press Intelligent Metasurface Sensors
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£58.12
Cambridge University Press Determining Provenance from Compositional Data
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£63.00
Cambridge University Press Statistical Methods for Recommender Systems
Book SynopsisThis book is for researchers and students in statistics, data mining, computer science, machine learning, marketing and also practitioners who implement recommender systems. It provides an in-depth discussion of challenges encountered in deploying real-life large-scale systems and state-of-the-art solutions in personalization, explore/exploit, dimension reduction and multi-objective optimization.Trade Review'This book provides a comprehensive guide to state-of-the-art statistical techniques that are used to power recommender systems. … The text is authoritative and well written, with the authors drawing on their extensive experience of researching, implementing and evaluating real-world recommender systems. The book considers the underlying mathematics of the techniques it describes and, as such, is aimed at a readership with a strong background in statistics and cognate subjects. However, while readers without such a background are likely to find the mathematics somewhat challenging, the prose descriptions are highly readable and enable readers to understand the key principles and ideas which underpin the various approaches. This book should be of interest to those involved with recommender systems as well as to those with a broader interest in machine learning.' Patrick Hill, BCS: The Chartered Institute for IT (www.bcs.org)Table of ContentsPart I. Introduction: 1. Introduction; 2. Classical methods; 3. Explore/exploit for recommender problems; 4. Evaluation methods; Part II. Common Problem Settings: 5. Problem settings and system architecture; 6. Most-popular recommendation; 7. Personalization through feature-based regression; 8. Personalization through factor models; Part III. Advanced Topics: 9. Factorization through latent dirichlet allocation; 10. Context-dependent recommendation; 11. Multi-objective optimization.
£56.16
Cambridge University Press Introduction to Graph Signal Processing
Book SynopsisAn intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.Table of Contents1. Introduction; 2. Node domain processing; 3. Graph signal frequency-Spectral graph theory; 4. Sampling; 5. Graph signal representations; 6. How to choose a graph; 7. Applications; Appendix A. Linear algebra and signal representations; Appendix B. GSP with Matlab: the GraSP toolbox; References; Index.
£69.99
Cambridge University Press Exponential Families in Theory and Practice
Book SynopsisDuring the past half-century, exponential families have attained a position at the center of parametric statistical inference. Theoretical advances have been matched, and more than matched, in the world of applications, where logistic regression by itself has become the go-to methodology in medical statistics, computer-based prediction algorithms, and the social sciences. This book is based on a one-semester graduate course for first year Ph.D. and advanced master''s students. After presenting the basic structure of univariate and multivariate exponential families, their application to generalized linear models including logistic and Poisson regression is described in detail, emphasizing geometrical ideas, computational practice, and the analogy with ordinary linear regression. Connections are made with a variety of current statistical methodologies: missing data, survival analysis and proportional hazards, false discovery rates, bootstrapping, and empirical Bayes analysis. The book connects exponential family theory with its applications in a way that doesn''t require advanced mathematical preparation.Trade Review'This book provides a unique perspective on exponential families, bringing together theory and methods into a unified whole. No other text covers the range of topics in this text. If you want to understand the 'why' as well as the `how' of exponential families, then this book should be on your bookshelf.' Larry Wasserman, Carnegie Mellon University'I am excited to see the publication of this monograph on exponential families by my friend and colleague Brad Efron. I learned some of this material during my Ph.D. studies at Stanford from the maestro himself, as well as the geometry of curved exponential families, Hoeffding's lemma, the Lindsey method, and the list goes on. They have lived with me my entire career and informed our work on GAMs and sparse GLMs. Generations of Stanford students have shared this privilege, and now generations in the future will be able to enjoy the unique Efron style.' Trevor Hastie, Stanford University'Exponential families can be magical in simplifying both theoretical and applied statistical analyses. Brad Efron's wonderful book exposes their secrets, from R. A. Fisher's early magic to Efron's own bootstrap: an essential text for understanding how data of all sizes can be approached scientifically.' Stephen Stigler, University of Chicago'This book provides an original and accessible study of statistical inference in the class of models called exponential families. The mathematical properties and flexibility of this class makes the models very useful for statistical practice – they underpin the class of generalized linear models, for example. Writing with his characteristic elegance and clarity, Efron shows how exponential families underpin, and provide insight into, many modern topics in statistical science, including bootstrap inference, empirical Bayes methodology, high-dimensional inference, analysis of survival data, missing data, and more.' Nancy Reid, University of Toronto'In this book, Brad Efron illuminates the exponential family as a practical, extendible, and crucial ingredient in all manners of data analysis, be they Bayesian, frequentist, or machine learning. He shows us how to shape, understand, and employ these distributions in both algorithms and analysis. The book is crisp, insightful, and indispensable.' David Blei, Columbia UniversityTable of Contents1. One-parameter exponential families; 2. Multiparameter exponential families; 3. Generalized linear models; 4. Curved exponential families, eb, missing data, and the em algorithm; 5. Bootstrap confidence intervals; Bibliography; Index.
£79.99
Cambridge University Press HighDimensional Data Analysis with LowDimensional Models
A systematic introduction to the theory, algorithms, and applications of key mathematical models for data science. Covering applications including imaging, communication, and face recognition, with online code, it is ideal for senior/graduate students in computer science, data science, and electrical engineering. With foreword by Emmanuel Candès.
£999.99
Cambridge University Press Topological Data Analysis with Applications
Book SynopsisThe continued and dramatic rise in the size of data sets has meant that new methods are required to model and analyze them. This timely account introduces topological data analysis (TDA), a method for modeling data by geometric objects, namely graphs and their higher-dimensional versions: simplicial complexes. The authors outline the necessary background material on topology and data philosophy for newcomers, while more complex concepts are highlighted for advanced learners. The book covers all the main TDA techniques, including persistent homology, cohomology, and Mapper. The final section focuses on the diverse applications of TDA, examining a number of case studies drawn from monitoring the progression of infectious diseases to the study of motion capture data. Mathematicians moving into data science, as well as data scientists or computer scientists seeking to understand this new area, will appreciate this self-contained resource which explains the underlying technology and how it can be used.Table of ContentsPart I. Background: 1. Introduction; 2. Data; Part II. Theory: 3. Topology; 4. Shape of data; 5. Structures on spaces of barcodes; Part III. Practice: 6. Case studies; References; Index.
£47.26
MIT Press Ltd Introduction to AI Robotics
Book Synopsis
£76.50
MIT Press Ltd How to Grow a Robot Developing HumanFriendly
Book SynopsisHow to develop robots that will be more like humans and less like computers, more social than machine-like, and more playful and less programmed.Most robots are not very friendly. They vacuum the rug, mow the lawn, dispose of bombs, even perform surgery—but they aren't good conversationalists. It's difficult to make eye contact. If the future promises more human-robot collaboration in both work and play, wouldn't it be better if the robots were less mechanical and more social? In How to Grow a Robot, Mark Lee explores how robots can be more human-like, friendly, and engaging.Developments in artificial intelligence—notably Deep Learning—are widely seen as the foundation on which our robot future will be built. These advances have already brought us self-driving cars and chess match-winning algorithms. But, Lee writes, we need robots that are perceptive, animated, and responsive—more like humans and less like computers, more social than mac
£21.60
MIT Press Ltd Probabilistic Machine Learning for Civil
Book SynopsisAn introduction to key concepts and techniques in probabilistic machine learning for civil engineering students and professionals; with many step-by-step examples, illustrations, and exercises.This book introduces probabilistic machine learning concepts to civil engineering students and professionals, presenting key approaches and techniques in a way that is accessible to readers without a specialized background in statistics or computer science. It presents different methods clearly and directly, through step-by-step examples, illustrations, and exercises. Having mastered the material, readers will be able to understand the more advanced machine learning literature from which this book draws.The book presents key approaches in the three subfields of probabilistic machine learning: supervised learning, unsupervised learning, and reinforcement learning. It first covers the background knowledge required to understand machine learning, including linear algebra and probabi
£45.00
Cengage Learning, Inc Mathematics for Information Technology
Book Synopsis
£203.40
John Wiley & Sons Inc Modern Machine Learning Techniques and Their
Book SynopsisThe integration of machine learning techniques and cartoon animation research is fast becoming a hot topic.Table of ContentsPreface xi 1 Introduction 1 1.1 Perception 2 1.2 Overview of Machine Learning Techniques 2 1.2.1 Manifold Learning 3 1.2.2 Semi-supervised Learning 5 1.2.3 Multiview Learning 8 1.2.4 Learning-based Optimization 9 1.3 Recent Developments in Computer Animation 11 1.3.1 Example-Based Motion Reuse 11 1.3.2 Physically Based Computer Animation 26 1.3.3 Computer-Assisted Cartoon Animation 33 1.3.4 Crowd Animation 42 1.3.5 Facial Animation 51 1.4 Chapter Summary 60 2 Modern Machine Learning Techniques 63 2.1 A Unified Framework for Manifold Learning 65 2.1.1 Framework Introduction 65 2.1.2 Various Manifold Learning Algorithm Unifying 67 2.1.3 Discriminative Locality Alignment 69 2.1.4 Discussions 71 2.2 Spectral Clustering and Graph Cut 71 2.2.1 Spectral Clustering 72 2.2.2 Graph Cut Approximation 76 2.3 Ensemble Manifold Learning 81 2.3.1 Motivation for EMR 81 2.3.2 Overview of EMR 81 2.3.3 Applications of EMR 84 2.4 Multiple Kernel Learning 86 2.4.1 A Unified Mulitple Kernel Learning Framework 87 2.4.2 SVM with Multiple Unweighted-Sum Kernels 89 2.4.3 QCQP Multiple Kernel Learning 89 2.5 Multiview Subspace Learning 90 2.5.1 Approach Overview 90 2.5.2 Techinique Details 90 2.5.3 Alternative Optimization Used in PA-MSL 93 2.6 Multiview Distance Metric Learning 94 2.6.1 Motivation for MDML 94 2.6.2 Graph-Based Semi-supervised Learning 95 2.6.3 Overview of MDML 95 2.7 Multi-task Learning 98 2.7.1 Introduction of Structural Learning 99 2.7.2 Hypothesis Space Selection 100 2.7.3 Algorithm for Multi-task Learning 101 2.7.4 Solution by Alternative Optimization 102 2.8 Chapter Summary 103 3 Animation Research: A Brief Introduction 105 3.1 Traditional Animation Production 107 3.1.1 History of Traditional Animation Production 107 3.1.2 Procedures of Animation Production 108 3.1.3 Relationship Between Traditional Animation and Computer Animation 109 3.2 Computer-Assisted Systems 110 3.2.1 Computer Animation Techniques 111 3.3 Cartoon Reuse Systems for Animation Synthesis 117 3.3.1 Cartoon Texture for Animation Synthesis 118 3.3.2 Cartoon Motion Reuse 120 3.3.3 Motion Capture Data Reuse in Cartoon Characters 122 3.4 Graphical Materials Reuse: More Examples 124 3.4.1 Video Clips Reuse 124 3.4.2 Motion Captured Data Reuse by Motion Texture 126 3.4.3 Motion Capture Data Reuse by Motion Graph 127 3.5 Chapter Summary 129 4 Animation Research: Modern Techniques 131 4.1 Automatic Cartoon Generation with Correspondence Construction 131 4.1.1 Related Work in Correspondence Construction 132 4.1.2 Introduction of the Semi-supervised Correspondence Construction 133 4.1.3 Stroke Correspondence Construction via Stroke Reconstruction Algorithm 138 4.1.4 Simulation Results 141 4.2 Cartoon Characters Represented by Multiple Features 146 4.2.1 Cartoon Character Extraction 147 4.2.2 Color Histogram 148 4.2.3 Hausdorff Edge Feature 148 4.2.4 Motion Feature 150 4.2.5 Skeleton Feature 151 4.2.6 Complementary Characteristics of Multiview Features 153 4.3 Graph-based Cartoon Clips Synthesis 154 4.3.1 Graph Model Construction 155 4.3.2 Distance Calculation 155 4.3.3 Simulation Results 156 4.4 Retrieval-based Cartoon Clips Synthesis 161 4.4.1 Constrained Spreading Activation Network 162 4.4.2 Semi-supervised Multiview Subspace Learning 165 4.4.3 Simulation Results 168 4.5 Chapter Summary 173 References 174 Index 195
£81.65
Rheinwerk Verlag GmbH Applied Machine Learning
£46.28
Rheinwerk Verlag GmbH Pytorch
£44.96
Apress Machine Learning For Network Traffic and Video Quality Analysis
Book SynopsisChapter 1: Introduction to NTMA and VQA.- Chapter 2: Network Traffic Monitoring and Analysis.- Chapter 3: Video Quality Assessment.- Chapter 4: Machine Learning Techniques for NTMA and VQA.- Chapter 5: NTMA Application with JavaScript.- Chapter 6: Video Quality Assessment Application Development with JavaScript.- Chapter 7: NTMA and VQA Integration.
£38.24
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Building Scalable Deep Learning Pipelines on AWS
Book SynopsisThis book is yourcomprehensive guide to creating powerful, end-to-end deep learning workflows on Amazon Web Services (AWS). The book explores how to integrate essential big data tools and technologiessuch as PySpark, PyTorch, TensorFlow, Airflow, EC2, and S3to streamline the development, training, and deployment of deep learning models. Starting with the importance of scaling advanced machine learning models, this book leverages AWS's robust infrastructure and comprehensive suite of services. It guides you through the setup and configuration needed to maximize the potential of deep learning technologies. You will gain in-depth knowledge of building deep learning pipelines, including data preprocessing, feature engineering, model training, evaluation, and deployment. The book provides insights into setting up an AWS environment, configuring necessary tools, and using PySpark for distributed data processing. You will also delve into hands-on tutorials for PyTorch and TensorFlow, mastering their roles in building and training neural networks. Additionally, you will learn how Apache Airflow can orchestrate complex workflows and how Amazon S3 and EC2 enhance model deployment at scale. By the end of this book, you will be equipped to tackle real-world challenges and seize opportunities in the rapidly evolving field of deep learning with AWS. You will gain the insights and skills needed to drive innovation and maintain a competitive edge in today's data-driven landscape. What You Will LearnMaximize AWS services for scalable and high-performancedeep learning architecturesHarness the capacity of PyTorch and TensorFlow for advanced neural network developmentUtilize PySpark for efficient distributed data processing onAWSOrchestrate complex workflows withApache Airflow for seamless data processing, model training, and deploymentWho This Book Is ForData scientists looking to expand their skill set to include deep learning on AWS, machine learning engineers tasked with designing and deploying machine learning systems who want to incorporate deep learning capabilities into their applications, AI practitioners working across various industries who seek to leverage deep learning for solving complex problems and gaining a competitive advantage
£38.24
Apress Introduction to Data Governance for Machine Learning Systems
Book SynopsisChapter 1: Introduction to Machine Learning Data Governance.- Chapter 2: Establishing a Data Governance Framework.- Chapter 3: Data Quality and Preprocessing.- Chapter .- 4: Data Privacy and Security Considerations.- Chapter 5: Ethical Implications and Bias Mitigation.- Chapter 6: Model Transparency and Interpretability.- Chapter 7: Monitoring and Maintaining Machine Learning System.- Chapter 8: Regulatory Compliance and Risk Management.- Chapter 9: Organizational Culture and Change Management.- Chapter 10: Future Trends and Emerging Challenges.
£31.34
Apress Designing for Human Intelligence in an Artificial Intelligence World
Book SynopsisChapter 1: OI, AI, and Research.- Chapter 2: Neurocognitive Foundations for People Other Than Dr. Rekart.- Chapter 3: All the Feels.- Chapter 4: Being Part of Something.- Chapter 5: Defining the Box.- Chapter 6: Attention (or lack thereof).- Chapter 7: The Evolution and Revolution of People.- Chapter 8: Communication is hard (and we suck at it).- Chapter 9: I remember when - or do I?.- Chapter 10: Making decisions - why we buy lottery tickets.- Chapter 11: Learning (and making mistakes).- Chapter 12: Business, Research, and Design Relationships- It’s Complicated.- Chapter 13: The AI Elephant in the Room.
£30.59
Apress The Complete Beginners Guide to Using ChatGPT
Book SynopsisChapter 1: Sorry, But You’re (Probably) Not Using the Best Prompts to Use ChatGPT to Its Highest Potential.- Chapter 2: Prompting ChatGPT to Help You Create New Content and Get Ideas.- Chapter 3: Teaching ChatGPT Information and Using Unique Prompts to Create Content in a Different Style.- Chapter 4: Getting Creative with the ChatGPT Canvas: Prompts to Help You Write Long-Form Content Like an Article or Thesis.- Chapter 5: Prompts to Make Your Life Easier with the Power of ChatGPT’s Data Analysis Abilities.- Chapter 6: Learn New Skills Quickly by Prompting ChatGPT to Act as a Teacher.- Chapter 7: Strategies and Prompts for Your Daily Life: Using ChatGPT as a Personal Assistant.- Chapter 8: Getting Chatty with ChatGPT in a Verbal Conversation.- Chapter 9: Using ChatGPT as a Time-Saver: Prompts Needed to Convert Anything in Your Daily Life.- Chapter 10: Save Yourself from Countless Revisions: Prompts for Using ChatGPT to Rewrite and Rephrase Text.- Chapter 11: Budget Planning, Product Research, and Writing an Article: Always Prompt ChatGPT with Lots of Data!.- Chapter 12: Visualize Your Ideas by Prompting ChatGPT’s DALL-E and Sora.
£29.69
Apress Data Science to Production
Book SynopsisChapter 1: Initial Setup on Your AWS Account (aws.amazon.com) .- Chapter 2: SSH to the EC2 Instance with VSCode and Necessary Setup.- Chapter 3: Load Balancer on your AWS console.- Chapter 4: Domain Name and SSL Certificates.- Chapter 5. Deploying More Robust Applications (Jenkins, Flask, and Streamlit).- Chapter 6. Create and Secure your Subdomains.- Chapter 7. How to setup this infrastructure on Google Cloud Platform (GCP).- Chapter 8. Advanced Deployment in GCP: Auto Scaling and Load Balancing Across Global Regions.- Chapter 9. Serverless Deployment with Google Cloud Run.- Chapter 10. Serverless Deployment with AWS.- Chapter 11. Demo: Using Jenkins as an ETL/ELT Platform for Data Science.- Chapter 12. Demo: Streamlit.- Chapter 13. Demo: Flask.
£33.99
Apress Android and IOS Mobile Forensics
Book SynopsisChapter 1: Introduction to Mobile Forensics.- Chapter 2: Android Forensics Fundamentals.- Chapter 3: iOS Forensics Fundamentals.- Chapter 4: Leveraging Blockchain for Mobile Forensics.- Chapter 5: Investigating Mobile Banking and Financial Applications.- Chapter 6 Examination of social media and Messaging Applications and Cloud Forensics.- Chapter 7: Location-based Data Analysis and Geolocation Artifacts.- Chapter 8: Mobile Network Forensics .- Chapter 9: Mobile Browser Forensics.- Chapter 10: Leveraging Machine Learning for Digital Investigations.- Chapter 11: Machine Learning Applications in Mobile Forensics.- Chapter 12: Deep Learning Techniques for Mobile Forensics.- Chapter 13: Privacy and Security Considerations in Mobile Forensics.- Chapter 14: Future Trends and Emerging Technologies in Mobile Forensics.- Chapter 15: Ethical and Legal Frameworks in Mobile Forensics.
£43.99
Apress AI Projects in PyTorch
Book SynopsisChapter 1: Introduction to Machine Learning.- Chapter 2: Tensors in PyTorch.- Chapter 3: Image Classification using Convolutional Neural Networks.- Chapter 4: Introduction to Natural Language Processing: Building a Text Classifier.- Chapter 5: Practical Natural Language Processing with Hugging Face.- Chapter 6: Building a Language Model for Storytelling.- Chapter 7: Audio Classification with PyTorch.- Chapter 8: Recommender Systems with PyTorch.- Chapter 9: Image Captioning.
£39.99
