Machine learning Books

Book SynopsisThe adoption of data analytics has remained remarkably static - perhaps reaching no more than thirty percent of potential users. This book explores the most important techniques for taking that adoption further: embedding analytics into the workflow of our everyday operations.

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Book SynopsisUsing hands-on examples, this practical book shows data scientists and data practitioners how to build their own custom knowledge graphs. Authors Jesus Barrasa and Jim Webber from Neo4j illustrate patterns commonly used for building knowledge graphs that solve many of today's pressing problems.

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Book SynopsisThis practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field.

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Book SynopsisIn this guide, data science leader Susan Shu Chang shows you how to tackle the ML hiring process.

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Book SynopsisThis handbook is ideal for learning how to design, build, and modernize cloud native data and machine learning platforms using AWS, Azure, Google Cloud, or multicloud tools like Fivetran, dbt, Snowflake, and Databricks.

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Book SynopsisCluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clTrade Review'Bouveyron, Celeux, Murphy, and Raftery pioneered the theory, computation, and application of modern model-based clustering and discriminant analysis. Here they have produced an exhaustive yet accessible text, covering both the field's state of the art as well as its intellectual development. The authors develop a unified vision of cluster analysis, rooted in the theory and computation of mixture models. Embedded R code points the way for applied readers, while graphical displays develop intuition about both model construction and the critical but often-neglected estimation process. Building on a series of running examples, the authors gradually and methodically extend their core insights into a variety of exciting data structures, including networks and functional data. This text will serve as a backbone for graduate study as well as an important reference for applied data scientists interested in working with cutting-edge tools in semi- and unsupervised machine learning.' John S. Ahlquist, University of California, San Diego'This book, written by authoritative experts in the field, gives a comprehensive and thorough introduction to model-based clustering and classification. The authors not only explain the statistical theory and methods, but also provide hands-on applications illustrating their use with the open-source statistical software R. The book also covers recent advances made for specific data structures (e.g. network data) or modeling strategies (e.g. variable selection techniques), making it a fantastic resource as an overview of the state of the field today.' Bettina Grün, Johannes Kepler Universität Linz, Austria'Four authors with diverse strengths nicely integrate their specialties to illustrate how clustering and classification methods are implemented in a wide selection of real-world applications. Their inclusion of how to use available software is an added benefit for students. The book covers foundations, challenging aspects, and some essential details of applications of clustering and classification. It is a fun and informative read!' Naisyin Wang, University of Michigan'This is a beautifully written book on a topic of fundamental importance in modern statistical science, by some of the leading researchers in the field. It is particularly effective in being an applied presentation - the reader will learn how to work with real data and at the same time clearly presenting the underlying statistical thinking. Fundamental statistical issues like model and variable selection are clearly covered as well as crucial issues in applied work such as outliers and ordinal data. The R code and graphics are particularly effective. The R code is there so you know how to do things, but it is presented in a way that does not disrupt the underlying narrative. This is not easy to do. The graphics are 'sophisticatedly simple' in that they convey complex messages without being too complex. For me, this is a 'must have' book.' Rob McCulloch, Arizona State University'This advanced text explains the underlying concepts clearly and is strong on theory … I congratulate the authors on the theoretical aspects of their book, it's a fine achievement.' Antony Unwin, International Statistical Review'In my opinion, the overall quality of this impactful and intriguing book can be expressed by concluding that it is a perfect fit to the Cambridge Series in Statistical and Probabilistic Mathematics, characterized as a series of high-quality upper-division textbooks and expository monographs containing applications and discussions of new techniques while emphasizing rigorous treatment of theoretical methods.' Zdenek Hlavka, MathSciNet'… this book not only gives the big picture of the analysis of clustering and classification but also explains recent methodological advances. Extensive real-world data examples and R code for many methods are also well summarized. This book is highly recommended to students in data science, as well as researchers and data analysts.' Li-Pang Chen, Biometrical Journal'Model-Based Clustering and Classification for Data Science: With Applications in R, written by leading statisticians in the field, provides academics and practitioners with a solid theoretical and practical foundation on the use of model-based clustering methods … this book will serve as an excellent resource for quantitative practitioners and theoreticians seeking to learn the current state of the field.' C. M. Foley, Quarterly Review of Biology'This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions … Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.' Hans-Jürgen Schmidt, zbMATHTable of Contents1. Introduction; 2. Model-based clustering: basic ideas; 3. Dealing with difficulties; 4. Model-based classification; 5. Semi-supervised clustering and classification; 6. Discrete data clustering; 7. Variable selection; 8. High-dimensional data; 9. Non-Gaussian model-based clustering; 10. Network data; 11. Model-based clustering with covariates; 12. Other topics; List of R packages; Bibliography; Index.

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Book SynopsisAn engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning toolsComprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architecturesPractical discussions of recurrent neural networks and non-supervised approaches to deep learningFulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.

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Book SynopsisToday's social and behavioral researchers increasingly need to know: What do I do with all this data? This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter Computational Time and Resources sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples.Trade Review"Current, highly informative, and useful, this is a 'go-to' book for social science graduate students, faculty, and practitioners seeking a strong introduction to machine learning. Unlike typical, more technical machine learning books, this one is unique in providing the strong psychological measurement guidance required to apply these techniques most appropriately. It walks the reader through general principles of machine learning, regression- and tree-based predictive models, text- and network-based methods of clustering, and--most innovatively--machine learning–based psychometric approaches (CFA and SEM)."--Fred Oswald, PhD, Professor and Herbert S. Autrey Chair in Social Sciences, Department of Psychological Sciences, Rice University "This book is very timely. Social scientists need to be educated about the pros and cons of machine learning methods and about how, when, and why these methods can be applied to their research topics. The book describes key techniques in enough detail to enable readers to subsequently digest more specialized journal articles or software applications, but not in so much detail as to lose momentum."--Sonya K. Sterba, PhD, Department of Psychology and Human Development, Vanderbilt University "Jacobucci, Grimm, and Zhang's ambitious book takes the reader on an in-depth tour of machine learning methods. Its strength is that the authors link machine learning to more traditional topics of regression, structural equation modeling, factor analysis, and network analysis methods. This book should be required reading for the new generation of psychology graduate students who are interested in more advanced quantitative methods."--James W. Pennebaker, PhD, Regents Centennial Professor of Liberal Arts and Professor of Psychology, The University of Texas at Austin ​"A 'must read' for social scientists who want to familiarize themselves with machine learning but don’t know where to start. Understanding the practices and principles of machine learning is fundamental to modern data analysis. Many social scientists will be surprised by how well their traditional statistical training has prepared them to grasp the material in the book."--Alexander Christensen, PhD, Department of Psychology and Human Development, Vanderbilt University-Table of ContentsI. Fundamental Concepts 1. Introduction - Why the Term Machine Learning? - Why do We Need Machine Learning? - How is this Book Different? - Definitions - Software - Datasets 2. The Principles of Machine Learning Research - Overview - Principle #1: Machine Learning is Not Just Lazy Induction - Principle #2: Orienting Our Goals Relative to Prediction, Explanation, and Description - Principle #3: Labeling a Study as Exploratory or Confirmatory is too Simplistic - Principle #4: Report Everything - Summary 3. The Practices of Machine Learning - Comparing Algorithms and Models - Model Fit - Bias-Variance Tradeoff - Resampling - Classification - Conclusion II. Algorithms for Univariate Outcomes 4. Regularized Regression - Linear Regression - Logistic Regression - Regularization - Rationale for Regularization - Alternative Forms of Regularization - Bayesian Regression - Summary 5. Decision Trees - Introduction - Decision Tree Algorithms - Miscellaneous Topics 6. Ensembles - Bagging - Random Forests - Gradient Boosting - Interpretation - Empirical Example - Important Notes - Summary III. Algorithms for Multivariate Outcomes 7. Machine Learning and Measurement - Defining Measurement Error - Impact of Measurement Error - Assessing Measurement Error - Weighting - Alternative Methods - Summary 8. Machine Learning and Structural Equation Modeling - Latent Variables as Predictors - Predicting Latent Variables - Using Latent Variables as Outcomes and Predictors - Can Regularization Improve Generalizability in SEM? - Nonlinear Relationships and Latent Variables - Summary 9. Machine Learning with Mixed-Effects Models - Mixed-Effects Models - Machine Learning with Clustered Data - Regularization with Mixed-Effects Models - Illustrative Example - Additional Strategies for Mining Longitudinal Data - Summary 10. Searching for Groups - Finite Mixture Model - Structural Equation Model Trees - Summary IV. Alternative Data Types 11. Introduction to Text Mining - Key Terminology - Data - Basic Text Mining - Text Data Preprocessing - Basic Analysis of the Teaching Comment Data - Sentiment Analysis - Topic Models - Summary 12. Introduction to Social Network Analysis - Network Visualization - Network Statistics - Basic Network Analysis - Network Modeling - Summary References

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Book SynopsisToday's social and behavioral researchers increasingly need to know: What do I do with all this data? This book provides the skills needed to analyze and report large, complex data sets using machine learning tools, and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory, early childhood learning, and more), with a focus on the interplay of statistical algorithm, data, and theory. The identification of heterogeneity, measurement error, regularization, and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis, structural equation models, and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter Computational Time and Resources sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples.Trade Review"Current, highly informative, and useful, this is a 'go-to' book for social science graduate students, faculty, and practitioners seeking a strong introduction to machine learning. Unlike typical, more technical machine learning books, this one is unique in providing the strong psychological measurement guidance required to apply these techniques most appropriately. It walks the reader through general principles of machine learning, regression- and tree-based predictive models, text- and network-based methods of clustering, and--most innovatively--machine learning–based psychometric approaches (CFA and SEM)."--Fred Oswald, PhD, Professor and Herbert S. Autrey Chair in Social Sciences, Department of Psychological Sciences, Rice University "This book is very timely. Social scientists need to be educated about the pros and cons of machine learning methods and about how, when, and why these methods can be applied to their research topics. The book describes key techniques in enough detail to enable readers to subsequently digest more specialized journal articles or software applications, but not in so much detail as to lose momentum."--Sonya K. Sterba, PhD, Department of Psychology and Human Development, Vanderbilt University "Jacobucci, Grimm, and Zhang's ambitious book takes the reader on an in-depth tour of machine learning methods. Its strength is that the authors link machine learning to more traditional topics of regression, structural equation modeling, factor analysis, and network analysis methods. This book should be required reading for the new generation of psychology graduate students who are interested in more advanced quantitative methods."--James W. Pennebaker, PhD, Regents Centennial Professor of Liberal Arts and Professor of Psychology, The University of Texas at Austin ​"A 'must read' for social scientists who want to familiarize themselves with machine learning but don’t know where to start. Understanding the practices and principles of machine learning is fundamental to modern data analysis. Many social scientists will be surprised by how well their traditional statistical training has prepared them to grasp the material in the book."--Alexander Christensen, PhD, Department of Psychology and Human Development, Vanderbilt University-Table of ContentsI. Fundamental Concepts 1. Introduction - Why the Term Machine Learning? - Why do We Need Machine Learning? - How is this Book Different? - Definitions - Software - Datasets 2. The Principles of Machine Learning Research - Overview - Principle #1: Machine Learning is Not Just Lazy Induction - Principle #2: Orienting Our Goals Relative to Prediction, Explanation, and Description - Principle #3: Labeling a Study as Exploratory or Confirmatory is too Simplistic - Principle #4: Report Everything - Summary 3. The Practices of Machine Learning - Comparing Algorithms and Models - Model Fit - Bias-Variance Tradeoff - Resampling - Classification - Conclusion II. Algorithms for Univariate Outcomes 4. Regularized Regression - Linear Regression - Logistic Regression - Regularization - Rationale for Regularization - Alternative Forms of Regularization - Bayesian Regression - Summary 5. Decision Trees - Introduction - Decision Tree Algorithms - Miscellaneous Topics 6. Ensembles - Bagging - Random Forests - Gradient Boosting - Interpretation - Empirical Example - Important Notes - Summary III. Algorithms for Multivariate Outcomes 7. Machine Learning and Measurement - Defining Measurement Error - Impact of Measurement Error - Assessing Measurement Error - Weighting - Alternative Methods - Summary 8. Machine Learning and Structural Equation Modeling - Latent Variables as Predictors - Predicting Latent Variables - Using Latent Variables as Outcomes and Predictors - Can Regularization Improve Generalizability in SEM? - Nonlinear Relationships and Latent Variables - Summary 9. Machine Learning with Mixed-Effects Models - Mixed-Effects Models - Machine Learning with Clustered Data - Regularization with Mixed-Effects Models - Illustrative Example - Additional Strategies for Mining Longitudinal Data - Summary 10. Searching for Groups - Finite Mixture Model - Structural Equation Model Trees - Summary IV. Alternative Data Types 11. Introduction to Text Mining - Key Terminology - Data - Basic Text Mining - Text Data Preprocessing - Basic Analysis of the Teaching Comment Data - Sentiment Analysis - Topic Models - Summary 12. Introduction to Social Network Analysis - Network Visualization - Network Statistics - Basic Network Analysis - Network Modeling - Summary References

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Book SynopsisOptimize, develop, and design PyTorch and TensorFlow models for a specific problem using the Microsoft Neural Network Intelligence (NNI) toolkit. This book includes practical examples illustrating automated deep learning approaches and provides techniques to facilitate your deep learning model development. The first chapters of this book cover the basics of NNI toolkit usage and methods for solving hyper-parameter optimization tasks. You will understand the black-box function maximization problem using NNI, and know how to prepare a TensorFlow or PyTorch model for hyper-parameter tuning, launch an experiment, and interpret the results. The book dives into optimization tuners and the search algorithms they are based on: Evolution search, Annealing search, and the Bayesian Optimization approach. The Neural Architecture Search is covered and you will learn how to develop deep learning models from scratch. Multi-trial and one-shot searching approaches of automatic neural networTable of ContentsChapter 1: Introduction to Neural Network Intelligence1.1 Installation1.2 Trial, search space, experiment1.3 Finding maxima of multivariate function1.4 Interacting with NNIChapter 2:Hyper-Parameter Tuning2.1 Preparing a model for hyper-parameter tuning2.2 Running experiment2.3 Interpreting results2.4 DebuggingChapter 3: Hyper-Parameter TunersChapter 4: Neural Architecture Search: Multi-trial4.1 Constructing a search space4.2 Running architecture search4.3 Exploration strategies4.4 Comparing exploration strategiesChapter 5: Neural Architecture Search: One-shot5.1 What is one-shot NAS?5.2 ENAS5.3 DARTSChapter 6: Model Compression6.1 What is model compression?6.2 Compressing your model6.3 Pruning6.4 QuantizationChapter 7: Advanced NNI

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Book SynopsisIntermediate-Advanced user levelTable of Contents1. Fragen in natürlicher Sprache stellen2. Die Insights-Funktion3. Entdeckung wichtiger Einflussfaktoren4. Drill-Down und Zerlegung von Hierarchien5. Hinzufügen intelligenter Visualisierungen6. Mit Szenarien experimentieren7. Einen Datensatz charakterisieren8. Spalten aus Beispielen erstellen9. Ausführen von R- und Python-Visualisierungen10. Datenumwandlung mit R und Python11. Ausführen von Machine Learning Modellen in der Azure Cloud

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Book SynopsisHow can machine learningespecially deep neural networksmake a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

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Book SynopsisThis easy-to-use reference for Tensorflow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.

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Book SynopsisIn this guide, author Gant Laborde--Google Developer Expert in machine learning and the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers.

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Book SynopsisYour training data has as much to do with the success of your data project as the algorithms themselves--most failures in deep learning systems relate to training data. But while training data is the foundation for successful machine learning, there are few comprehensive resources to help you ace the process. This hands-on guide explains how to work with and scale training data. Data science professionals and machine learning engineers will gain a solid understanding of the concepts, tools, and processes needed to: Design, deploy, and ship training data for production-grade deep learning applications Integrate with a growing ecosystem of tools Recognize and correct new training data-based failure modes Improve existing system performance and avoid development risks Confidently use automation and acceleration approaches to more effectively create training data Avoid data loss by structuring metadata around created datasets Clearly explain training data concepts to subject matter experts

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Book SynopsisVisual Tracking in Conventional Minimally Invasive Surgery introduces the various tools and methodologies that can be used to enhance a conventional surgical setup with some degree of automation. The main focus of this book is on methods for tracking surgical tools and how they can be used to assist the surgeon during the surgical operation. Various notions associated with surgeoncomputer interfaces and image-guided navigation are explored, with a range of experimental results.The book starts with some basic motivations for minimally invasive surgery and states the various distinctions between robotic and non-robotic (conventional) versions of this procedure. Common components of this type of operation are presented with a review of the literature addressing the automation aspects of such a setup. Examples of tracking results are shown for both motion and gesture recognition of surgical tools, which can be used as parTable of ContentsIntroduction. Endoscope Setup and Calibration. Marker-Based Tracking. Marker-less Tracking: Gaussian Type. Marker-lessTracking: Non-Gaussian Type. Reign-Based Tracking. Appendix A: Morphological operation and Neural Network. Appendix B:Adaptive Gaussian Mixture Model. Appendix C: Overview of Particle Filter. Appendix D: Planar Homography. Appendix E:Overview of Region Matching Approaches.

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Book SynopsisMachine learning without advanced math! This book presents a serious, practical look at machine learning, preparing you for valuable insights on your own data. The Art of Machine Learning is packed with real dataset examples and sophisticated advice on how to make full use of powerful machine learning methods. Readers will need only an intuitive grasp of charts, graphs, and the slope of a line, as well as familiarity with the R programming language. You'll become skilled in a range of machine learning methods, starting with the simple k-Nearest Neighbours method (k-NN), then on to random forests, gradient boosting, linear/logistic models, support vector machines, the LASSO, and neural networks. Final chapters introduce text and image classification, as well as time series. You'll learn not only how to use machine learning methods, but also why these methods work, providing the strong foundational background you'll need in practice. Additional features: How to avoid common problems, suTrade Review"In contrast to other books about machine learning, there is a bigger emphasis on programming and usage in practice. In particular, there is an excellent explanation of how to avoid over/under-fitting, and how to use cross-validation. This book is sure to be helpful for students who are interested to understand the core concepts, as well as their practical implementations in R."—Toby Dylan Hocking, Assistant Professor, Northern Arizona University"The Art of Machine Learning by Norman Matloff is a welcome addition to a growing body of books about machine learning. Matloff, whose career spans both computer science and statistics, addresses the new and exciting field with a fresh approach."—Dirk Eddelbuettel, Department of Statistics, University of IllinoisTable of ContentsAcknowledgmentsIntroductionPART I: PROLOGUE, AND NEIGHBORHOOD-BASED METHODSChapter 1: Regression ModelsChapter 2: Classification ModelsChapter 3: Bias, Variance, Overfitting, and Cross-ValidationChapter 4: Dealing with Large Numbers of FeaturesPART II: TREE-BASED METHODSChapter 5: A Step Beyond k-NN: Decision TreesChapter 6: Tweaking the TreesChapter 7: Finding a Good Set of HyperparametersPART III: METHODS BASED ON LINEAR RELATIONSHIPSChapter 8: Parametric MethodsChapter 9: Cutting Things Down to Size: RegularizationPART IV: METHODS BASED ON SEPARATING LINES AND PLANESChapter 10: A Boundary Approach: Support Vector MachinesChapter 11: Linear Models on Steroids: Neural NetworksPART V: APPLICATIONSChapter 12: Image Classification Chapter 13: Handling Time Series and Text Data Appendix A: List of Acronyms and Symbols Appendix B: Statistics and ML Terminology CorrespondenceAppendix C: Matrices, Data Frames, and Factor ConversionsAppendix D: Pitfall: Beware of “p-Hacking”!

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Book SynopsisThe Shape of Data shows how to use geometry- and topology-based algorithms for machine learning. Focused on practical applications rather than dense mathematical concepts, the book progresses through coding examples using social network data, text data, medical data, and education data. Readers will come away with an entirely new toolkit to use in their own machine-learning work, as well as with a solid understanding of some of the most exciting algorithms being used in the field today.Trade Review"The title says it all. Data is bound by many complex relationships not easily shown in our two-dimensional, spreadsheet filled world. The Shape of Data walks you through this richer view and illustrates how to put it into practice."—Stephanie Thompson, Data Scientist and Speaker“The Shape of Data is a novel perspective and phenomenal achievement in the application of geometry to the field of machine learning. It is expansive in scope and contains loads of concrete examples and coding tips for practical implementations, as well as extremely lucid, concise writing to unpack the concepts. Even as a more veteran data scientist who has been in the industry for years now, having read this book I've come away with a deeper connection to and new understanding of my field."—Kurt Schuepfer, Ph.D., McDonalds Corporation“A great source for the application of topology and geometry in data science. Topology and geometry advance the field of machine learning on unstructured data, and The Shape of Data does a great job introducing new readers to the subject.”—Uchenna “Ike” Chukwu, Senior Quantum Developer"See how data looks not just as lists of numbers but as plots and graphs. The Shape of Data shows the reader how to visualize data sets and discover relations hidden in the numbers and sets. . . . In this age of large data sets and deep learning, data graphics are essential to scientists and engineers—just like this book."—David S. Mazel, Principal/Manager Systems Engineer, Regulus-Group"Everyone who works at the border of geometry and Data Science will find the book and invaluable resource and source of inspiration. It is considerate that the R-codes used in the book have readily accessible python codes. "—Geoffrey Mboya, DPhil (Oxon), Director at Mfano Africa"Comprehensive and exceptionally well written, The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R is impressively 'reader friendly' in organization and presentation, making it an ideal instructional resource for anyone with an interest in topology, computer hacking, or mathematical/statistical computer software."—Midwest Book Review

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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

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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

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Book SynopsisMost machine learning systems that are deployed in the world today learn from human feedback. However, most machine learning courses focus almost exclusively on the algorithms, not the human-computer interaction part of the systems. This can leave a big knowledge gap for data scientists working in real-world machine learning, where data scientists spend more time on data management than on building algorithms. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation, active learning, transfer learning, and using machine learning to optimize every step of the process. Key Features · Active Learning to sample the right data for humans to annotate · Annotation strategies to provide the optimal interface for human feedback · Supervised machine learning design and query strategies to support Human-in-the-Loop systems · Advanced Adaptive Learning approaches · Real-world use cases from well-known data scientists For software developers and data scientists with some basic Machine Learning experience. About the technology “Human-in-the-Loop machine learning” refers to the need for human interaction with machine learning systems to improve human performance, machine performance, or both. Ongoing human involvement with the right interfaces expedites the efficient labeling of tricky or novel data that a machine can’t process, reducing the potential for data-related errors. Robert Munro has built Annotation, Active Learning, and machine learning systems with machine learning-focused startups and with larger companies including Amazon, Google, IBM, and most major phone manufacturers. If you speak to your phone, if your car parks itself, if your music is tailored to your taste, or if your news articles are recommended for you, then there is a good chance that Robert contributed to this experience. Robert holds a PhD from Stanford focused on Human-in-the-Loop machine learning for healthcare and disaster response, and is a disaster response professional in addition to being a machine learning professional. A worked example throughout this text is classifying disaster-related messages from real disasters that Robert has helped respond to in the past.

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Book SynopsisThe only way to learn is to practice! In Machine Learning Bookcamp, you’ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you’ve learned in previous chapters. By the end of the bookcamp, you’ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technologyMachine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that’s exactly what you’ll be doing in Machine Learning Bookcamp. about the bookIn Machine Learning Bookcamp you’ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you’ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You’ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you’re done working through these fun and informative projects, you’ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what's inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the readerFor readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning.

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Book Synopsis Build your own chatbot using Python and open source tools. This book begins with an introduction to chatbots where you will gain vital information on their architecture. You will then dive straight into natural language processing with the natural language toolkit (NLTK) for building a custom language processing platform for your chatbot. With this foundation, you will take a look at different natural language processing techniques so that you can choose the right one for you.  The next stage is to learn to build a chatbot using the API.ai platform and define its intents and entities. During this example, you will learn to enable communication with your bot and also take a look at key points of its integration and deployment. The final chapter of Building Chatbots with Python teaches you how to build, train, and deploy your very own chatbot. Using open source libraries and machine learning techniques you will learnTable of ContentsChapter 1: Introducing Chatbots Chapter Goal: Make the user get familiar with Chatbots.Sub -Topics1. Do’s and Don'ts in Chatbots2. What are the limitations of chatbots and how we should solve them?3. What are different kind of chatbots ? Where do they fit in ?Chapter 2: Natural Language ProcessingChapter Goal: Be able to do custom natural language processing platform for your chatbotsSub - Topics 1. Installation of NLTK and methods in natural language processing.2. POS Tagging, Stemming, Lemmetization, 3. Logical SemanticsChapter 3: Chatbot DevelopmentChapter Goal: Building a chatbot and defining its data constraintsSub - Topics: 1. Using api.ai platform to create a chatbot2. Feeding data and defining Intents and entitiesChapter 4: Chatbot CommunicationChapter Goal: Enabling communication with the bot to make the bot respond to your queries.Sub - Topics: 1. Making our chatbot respond to our queries2. Integration and DeploymentChapter 5: Build-Train-DeployChapter Goal: To build, train and deploy a chatbot of your own Sub - Topics: 1. Getting acclimatize to use open source libraries to train your data2. Defining Intents and entities on your data3. Using ML algorithms to predict the intent and take action based on that4. Using your code in a web app to make a conversational agent.5. Deploy your app on your own server with AWS

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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

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Book SynopsisTable of ContentsPart I: Introduction 1. Overview of Data Science, Analytics, and Machine Learning 2. Introduction to the R Language Part II: Applied Statistics and Data Visualization 3. Variables and Measurement Scales 4. Descriptive and Probabilistic Statistics 5. Hypotheses Tests 6. Data Visualization and Multivariate Graphs Part III: Data Mining and Preparation 7. Building Handcrafted Robots 8. Using APIs to Collect Data 9. Managing Data Part IV: Unsupervised Machine Learning Techniques 10. Cluster Analysis 11. Factorial and Principal Component Analysis (PCA) 12. Association Rules and Correspondence Analysis Part V: Supervised Machine Learning Techniques 13. Simple and Multiple Regression Analysis 14. Binary, Ordinal and Multinomial Regression Analysis 15. Count-Data and Zero-Inflated Regression Analysis 16. Generalized Linear Mixed Models Part VI: Improving Performance and Introduction to Deep Learning 17. Support Vector Machine 18. CART (Classification and Regression Trees) 19. Bagging, Boosting and Uplift (Persuasion) Modeling 20. Random Forest 21. Artificial Neural Network 22. Introduction to Deep Learning Part VII: Spatial Analysis 23. Working on Shapefiles 24. Dealing with Simple Features Objects 25. Raster Objects 26. Exploratory Spatial Analysis Part VII: Adding Value to your Work 27. Enhanced and Interactive Graphs 28. Dashboards with R

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Book SynopsisA practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models.Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author?s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems. ?Integrates coverage of cutting-edge research, existing tools, and real-world applications?Provides students and professionals with an engineering view for production-ready machine learning systems?Proven in the classroom?Offers supplemental resources including slides, videos, exams, and further readings

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Book SynopsisTable of ContentsPart 1. Introduction 1. Introduction Part 2. Theory and Algorithm 2. Model Inference on Edge Device 3. Model Training on Edge Device 4. Network Encoding and Quantization Part 3. Architecture Optimization 5. DANoC: An Algorithm and Hardware Codesign Prototype 6. Ensemble Spiking Networks on Edge Device 7. SenseCamera: A Learning Based Multifunctional Smart Camera Prototype

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Book SynopsisTable of Contents1. Fundamentals of Image Enhancement: Techniques and applications 2. Fundamentals of Image Reconstruction: Concepts and Challenges 3. Soft Computing based Image Reconstruction and Enhancement 4. Image Enhancement for Underwater Images 5. Image Enhancement and Reconstruction for Smart Healthcare 6. Super-resolution of medical images – to come 7. Image Enhancement Techniques Used in Remote Sensing Satellite Imagery 8. Low Contrast Image Enhancement 9. Image Dehazing – to come 10. AI for Image enhancement and reconstruction – to come 11. Enhancement and Reconstruction of Night Vision Images 12. Color Image Reconstruction and Enhancement 13. Video Enhancement and Super-resolution 14. Biometric Image Enhancement and Reconstruction 15. 3D Image Reconstruction and Enhancement 16. Deep-Learning-Based Image Reconstruction and Enhancement 17. Image Reconstruction and Enhancement for Retinal Fundus Images 18. Image Enhancement in Agriculture

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Book SynopsisThe primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields.This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course.The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitTable of Contents1. 1. Preamble. 2. The Language of Optimization. 3. Computational Complexity. 4. Algebra Review. 5. Matrix Factorization. 6. Linear Programming. 7. Sensitivity Analysis. 8. Integer Linear Programing. 9. Calculus Review. 10. A Calculus Approach to Nonlinear Programming. 11. Constrained Nonlinear Programming: Lagrange Multipliers and the KKT Conditions. 12. Optimization involving Quadratic Forms. 13. Iterative Methods. 14. Derivative-Free Methods. 15. Search Algorithms. 16. Important Sets for Optimization. 17. The Fundamental Theorem of Linear Programming. 18. Convex Functions. 19. Convex Optimization. 20. An Introduction to Combinatorics. 21. An Introduction to Graph Theory. 22. Network Flows. 23. Minimum-Weight Spanning Trees and Shortest Paths. 24. Network Modeling and the Transshipment Problem. 25. The Traveling Salesperson Problem. Probability. 27. Regression Analysis via Least Squares. 28. Forecasting. 29. Introduction to Machine Learning.

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Book SynopsisMachine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics.The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additivTrade Review"Machine learning is considered promising for investment management applications, yet the associated low signal to noise ratio presents a high bar for improving on the incumbent quant asset management tooling. The book of Coqueret and Guida is a treat for those who do not want to lose sight of the machine learning forest for the trees. Whether you are an academic scholar or a finance practitioner, you will learn just what you need to rigorously investigate machine learning techniques for factor investing applications, along with plenty of useful code snippets." -Harald Lohre, Executive Director of Research at Robeco and Honorary Researcher at Lancaster University Management School"Written by two experts on quantitative finance, this book covers everything from basic materials to advanced techniques in the field of quantitative investment strategies: data processing, alpha signal generation, portfolio optimization, backtesting and performance evaluation. Concrete examples related to asset management problems illustrate each machine learning technique, such as neural network, lasso regression, autoencoder or reinforcement learning. With more than 20 coding exercises and solutions provided in Python, this publication is a must for both students, academics and professionals who are looking for an up-to-date technical exposition on quantitative asset management from basic smart beta portfolios to enhanced alpha strategies including factor investing."-Thierry Roncalli, Head of Quantitative Portfolio Strategy at Amundi Institute, Amundi Asset ManagementTable of ContentsPart 1. Introduction 1. Notations and data 2. Introduction 3. Factor investing and asset pricing anomalies 4. Data preprocessing Part 2. Common supervised algorithms 5. Penalized regressions and sparse hedging for minimum variance portfolios 6. Tree-based methods 7. Neural networks 8. Support vector machines 9. Bayesian methods Part 3. From predictions to portfolios 10. Validating and tuning 11. Ensemble models 12. Portfolio backtesting Part 4. Further important topics 13. Interpretability 14. Two key concepts: causality and non-stationarity 15. Unsupervised learning 16. Reinforcement learning Part 5. Appendix 17. Data description 18. Solutions to exercises

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Book SynopsisHuman-Robot Interaction: Safety, Standardization, and Benchmarking provides a comprehensive introduction to the new scenarios emerging where humans and robots interact in various environments and applications on a daily basis. The focus is on the current status and foreseeable implications of robot safety, approaching these issues from the standardization and benchmarking perspectives. Featuring contributions from leading experts, the book presents state-of-the-art research, and includes real-world applications and use cases. It explores the key leading sectorsârobotics, service robotics, and medical roboticsâand elaborates on the safety approaches that are being developed for effective human-robot interaction, including physical robot-human contacts, collaboration in task execution, workspace sharing, human-aware motion planning, and exploring the landscape of relevant standards and guidelines.Features Presenting aTable of Contents 1 The Role of Standardization in Technical Regulations André Pirlet 2 The intricate relationships between private standards and publicpolicymakingin the case of personal care robot. Who cares more? Eduard Fosch-Villaronga and Angelo Jr Golia 3 Standard Ontologies and HRI Sandro Rama Fiorini, Abdelghani Chibani, Tamas Haidegger, Joel Luis Carbonera, Craig Schlenoff, Jacek Malec, Edson Prestes, Paulo Gonçalves, S. Veera Ragavan, Howard Li, Hirenkumar Nakawala, Stephen Balakirsky, Sofiane Bouznad, Noauel Ayari, and Yacine Amirat 4 Robot Modularity and safety for Service Robots Hong Seong Park and Gurvinder Singh Virk 5 Human-robot shared workspace in aerospace factories Gilber Tang 6 Workspace sharing in mobile manipulation José Saenz 7 On rehabilitation robotics safety, benchmarking, standards Jan F. Veneman 8 A practical appraisal of ISO 13482 as a reference for an orphan robot category Paolo Barattini 9 Safety of Medical Robots, Regulation and Standards Kiyo Chinzei 10 The Other End of Human–Robot Interaction: Models for Safe and Efficient Tool–Tissue Interactions Arpad Takacs, Imre J. Rudas, Tamas Haidegger 11 Passive Bilateral Teleoperation with Safety Considerations Lorinc Marton 12 Human-Robot Interfaces in Autonomous Surgical Robots Paolo Fiorini and Riccardo Muradore

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Book SynopsisMachine Learning is an integral tool in a business analyst's arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing Table of Contents1. Introduction to Machine Learning for Data Analytics 2. Role of Machine Learning in Promoting Sustainability 3. Addressing the Utilization of Popular Regression Models in business applications 4. CHATBOTS: The Uses and Impact In The Hospitality Sector 5. Traversing Through the Use of Robotics in Medical Industry: Outlining Emerging Trends and Perspectives for Future Growth 6. Integration of AI in Insurance and Health Care: What Does It Mean? 7. Artificial Intelligence in Agriculture – A Review 8. Machine Learning and Artificial Intelligence-based Tools in Digital Marketing: An Integrated Approach 9. Application Of Artificial Intelligence In Market Knowledge And B2b Marketing Co-Creation 10 A Systematic Literature Review of Artificial Intelligence's Impact on Customer Experience 11. The Impact of Artificial Intelligence on Customer Experience and the Purchasing Process 12. Application of Artificial Intelligence in Banking – A Review 13. Digital Ethics: Towards a socially preferable development of AI systems

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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

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Book SynopsisNumerical simulation models are used in all engineering disciplines for modeling physical phenomena to learn how the phenomena work, and to identify problems and optimize behavior. Smart Proxy Models provide an opportunity to replicate numerical simulations with very high accuracy and can be run on a laptop within a few minutes, thereby simplifying the use of complex numerical simulations, which can otherwise take tens of hours. This book focuses on Smart Proxy Modeling and provides readers with all the essential details on how to develop Smart Proxy Models using Artificial Intelligence and Machine Learning, as well as how it may be used in real-world cases. Covers replication of highly accurate numerical simulations using Artificial Intelligence and Machine Learning Details application in reservoir simulation and modeling and computational fluid dynamics Includes real case studies based on commercially available simulators Table of Contents 1. Artificial Intelligence and Machine Learning. 2. Numerical simulation and modeling. 3. Proxy modeling. 4. Smart Proxy Modeling for numerical reservoir simulation. 5. Smart Proxy Modeling for computational fluid dynamics (CFD).

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Book SynopsisImage processing and machine learning are used in conjunction to analyze and understand images. Where image processing is used to pre-process images using techniques such as filtering, segmentation, and feature extraction, machine learning algorithms are used to interpret the processed data through classification, clustering, and object detection. This book serves as a textbook for students and instructors of image processing, covering the theoretical foundations and practical applications of some of the most prevalent image processing methods and approaches.Divided into two volumes, this first installment explores the fundamental concepts and techniques in image processing, starting with pixel operations and their properties and exploring spatial filtering, edge detection, image segmentation, corner detection, and geometric transformations. It provides a solid foundation for readers interested in understanding the core principles and practical applications of image prTable of ContentsPreface Volume 1. 1. Pixel Operations. 2. Spatial Filtering. 3. Edge Detection. 4. Segmentation and Processing of Binary Images. 5. Corner Detection. 6. Line Detection. Index.

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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.

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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

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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

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Book SynopsisDeep Learning Approach for Natural Language Processing, Speech, and Computer Vision provides an overview of general deep learning methodology and its applications of natural language processing (NLP), speech, and computer vision tasks. It simplifies and presents the concepts of deep learning in a comprehensive manner, with suitable, full-fledged examples of deep learning models, with an aim to bridge the gap between the theoretical and the applications using case studies with code, experiments, and supporting analysis.Features: Covers latest developments in deep learning techniques as applied to audio analysis, computer vision, and natural language processing. Introduces contemporary applications of deep learning techniques as applied to audio, textual, and visual processing. Discovers deep learning frameworks and libraries for NLP, speech, and computer vision in Python. Gives insights into usTable of Contents1 Introduction 2 Natural Language Processing 3 State-of-the-Art Natural Language 4 Applications of Natural Language Processing Fundamentals of Speech Recognition 6 Deep Learning Models for Speech Recognition 7 End-to-End Speech Recognition Models 8 Computer Vision Basics 9 Deep Learning Models for Computer Vision 10 Applications of Computer Vision

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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.

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Book SynopsisThis book investigates in detail the emerging deep learning (DL) technique in computational physics, assessing its promising potential to substitute conventional numerical solvers for calculating the fields in real-time. After good training, the proposed architecture can resolve both the forward computing and the inverse retrieve problems.Pursuing a holistic perspective, the book includes the following areas. The first chapter discusses the basic DL frameworks. Then, the steady heat conduction problem is solved by the classical U-net in Chapter 2, involving both the passive and active cases. Afterwards, the sophisticated heat flux on a curved surface is reconstructed by the presented Conv-LSTM, exhibiting high accuracy and efficiency. Additionally, a physics-informed DL structure along with a nonlinear mapping module are employed to obtain the space/temperature/time-related thermal conductivity via the transient temperature in Chapter 4. Finally, in Chapter 5, a series of thTable of Contents1. Deep Learning Framework and Paradigm in Computational Physics 2. Application of U-net in 3D Steady Heat Conduction Solver 3. Inversion of complex surface heat flux based on ConvLSTM 4. Time-domain electromagnetic inverse scattering based on deep learning 5. Reconstruction of thermophysical parameters based on deep learning 6. Advanced Deep Learning Techniques in Computational Physics

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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.

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Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.

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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.

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Book Synopsis Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python. This book covers architectural patterns in data analytics, text and image classification, optimization techniques, natural language processing, and computer vision in the cloud environment. Generic design patterns in Python programming is clearly explained, emphasizing architectural practices such as hot potato anti-patterns. You''ll review recent advances in databases such as Neo4j, Elasticsearch, and MongoDB. You''ll then study feature engineering in images and texts with implementing business logic and see how to build machine learning and deep learning models using transfer learning.  Advanced Analytics with Python, 2nd edition features a chapter on clustering with a neural network, regularization techniques, and algorithmic design patterns in data analyticTable of Contents CHAPTER 1: Overview of Python Language 1.1 Philosophy of Python programming 1.2 Comparison with other languages 1.4 Design patterns in Python 1.4.1 Structural patterns 1.4.2 Behavioral patterns 1.4.3 Creational patterns 1.5 Why Python is so popular? 1.6 Use-case where Python does not fit well 1.7 Interfacing Python with other languages 1.7.1 Running Stanford NLP Java library in Python 1.7.2 Running time series Holt- Winter R module in Python 1.7.3 Expose your Python program as service in 2 minutes 1.8 Essential architectural pattern in data analytics 1. Hot Potato anti pattern 2. Data collector as a service 3. Bridge & proxy patterns. 4. Application layering CHAPTER 2: ETL with Python 2.1 Introduction 2.2 Python &Mysql 2.3 Python & Neo4j 2.4 Python & Elastic Search 2.5 Crawling with Beautiful Soup 2.6 Crawling using selenium 2.7 Regular expressions 2.8 Panda framework 2.9 Cloud Storages 2.9.1 AWS storage 2.10.1 GCP storages 2.9 Topical crawling 2.9.1 Find potential activists for a political party from web CHAPTER 3: Supervised Learning and Unsupervised Learning with Python 3.1. Introduction 3.2 Correlation analysis 3.2.1 Measures of correlation 3.2.2 Threshold for correlation 3.2.3 Dealing uneven cordiality of features 3.3 Principle component analysis 3.3.1 Singular value decomposition algorithm 3. 3.2 Factor analysis 3.3.3 Use case: Measuring impact of change in organization 3.4 Mutual information & dealing with categorical data 3.4.1 Use case: Measuring most significant features in ad price prediction 3.5 Feature engineering in texts and images 3.5.1 Classification 3. 5.2 Decision tree & entropy gain 3. 5.3 Random forest classifier 3. 5.4 Naïve bay’s classifier 3. 5.5 Support vector machine 3. 5.6 Text classification using Python 3. 5.7 Image classification using Python 3. 5.8 Supervised & unsupervised learning 3. 5.9. Semi supervised learning 3. 6.1 Regression 3. 6.2 Least-square estimation 3. 6.3 Logistic regression 3. 6.4 Classification using regression 3.6.5 Feature scaling 3.6.6 Intentionally bias the model to over fit or under fit CHAPTER 4: Clustering with Python 4.1 Introduction 4.2 Distance measures 4.3 Hierarchical clustering 4.3.1 Top to bottom algorithm 4.3.2 Bottom to top algorithm 4.3.3 Dendrogram to cluster 4.3.4 Choosing the threshold 4.4 K-Mean clustering 4.4.1 Algorithm 4.4.2 Choosing K 4.5 Graph theoretic approach 4.6 Measure for good clustering 4.7 Find summary of a paragraph 4.8 Find faces in images CHAPTER 5: Deep Learning & Neural Networks 5.1 History 5.2 Architecture 5.3 Use-case where NN fit well 5.4 Back propagation algorithm 5.5 Quick tour to other NN algorithms 5.6 Regularization techniques 5.7 Recurrent neural network 5.8 Goal oriented dialog system 5. 9.1 Convolution neural network 5. 9.2 Fake image detection Introduction to reinforcement learning 1. Dancing Floor on GCP 2. Dialectic Learning CHAPTER 6: Time Series Analysis 6.1 Introduction 6.2 Smoothing techniques 6.3 Autoregressive model 6.4 Moving average model 6.5 ARMA model 6.6 ARIMA model 6.7. SARIMA model 6.8 Historical practice 6.9 Frequency domain analysis in time series CHAPTER 7: Analytics in Scale 7.1 Introduction 7.2 Hadoop architecture 7.3 Popular design pattern in MapReduce 7.4 Introduction to cloud 7.5. Analytics on cloud 7.6 Introduction to Spark 7.7. Spark architecture - Memory optimization - Problem with memory optimization - Essential parameter in Spark - Naïve Bayes classifier in Spark 7.8 A recommendation system in Spark

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