Machine learning Books
World Scientific Publishing Co Pte Ltd Least Squares Support Vector Machines
Book SynopsisThis book focuses on Least Squares Support Vector Machines (LS-SVMs) which are reformulations to standard SVMs. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations from optimization theory. The authors explain the natural links between LS-SVM classifiers and kernel Fisher discriminant analysis. Bayesian inference of LS-SVM models is discussed, together with methods for imposing sparseness and employing robust statistics.The framework is further extended towards unsupervised learning by considering PCA analysis and its kernel version as a one-class modelling problem. This leads to new primal-dual support vector machine formulations for kernel PCA and kernel CCA analysis. Furthermore, LS-SVM formulations are given for recurrent networks and control. In general, support vector machines may pose heavy computational challenges for large data sets. For this purpose, a method of fixed size LS-SVM is proposed where the estimation is done in the primal space in relation to a Nyström sampling with active selection of support vectors. The methods are illustrated with several examples.Table of ContentsSupport Vector Machines; Basic Methods of Least Squares Support Vector Machines; Bayesian Inference for LS-SVM Models; Robustness; Large Scale Problems; LS-SVM for Unsupervised Learning; LS-SVM for Recurrent Networks and Control.
£90.00
World Scientific Publishing Co Pte Ltd Support Vector Machine In Chemistry
Book SynopsisIn recent years, the support vector machine (SVM), a new data processing method, has been applied to many fields of chemistry and chemical technology. Compared with some other data processing methods, SVM is especially suitable for solving problems of small sample size, with superior prediction performance. SVM is fast becoming a powerful tool of chemometrics. This book provides a systematic approach to the principles and algorithms of SVM, and demonstrates the application examples of SVM in QSAR/QSPR work, materials and experimental design, phase diagram prediction, modeling for the optimal control of chemical industry, and other branches in chemistry and chemical technology.
£110.70
World Scientific Publishing Co Pte Ltd Machine Learning Applications In Software
Book SynopsisMachine learning deals with the issue of how to build computer programs that improve their performance at some tasks through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. Not surprisingly, the field of software engineering turns out to be a fertile ground where many software development and maintenance tasks could be formulated as learning problems and approached in terms of learning algorithms. This book deals with the subject of machine learning applications in software engineering. It provides an overview of machine learning, summarizes the state-of-the-practice in this niche area, gives a classification of the existing work, and offers some application guidelines. Also included in the book is a collection of previously published papers in this research area.Table of ContentsIntroduction to Machine Learning and Software Engineering; ML Applications in Prediction and Estimation; ML Applications in Property and Model Discovery; ML Applications in Transformation; ML Applications in Generation and Synthesis; ML Applications in Reuse; ML Applications in Requirement Acquisition; ML Applications in Management of Development Knowledge
£146.70
World Scientific Publishing Co Pte Ltd Multilingual Text Analysis: Challenges, Models,
Book SynopsisText analytics (TA) covers a very wide research area. Its overarching goal is to discover and present knowledge — facts, rules, and relationships — that is otherwise hidden in the textual content. The authors of this book guide us in a quest to attain this knowledge automatically, by applying various machine learning techniques.This book describes recent development in multilingual text analysis. It covers several specific examples of practical TA applications, including their problem statements, theoretical background, and implementation of the proposed solution. The reader can see which preprocessing techniques and text representation models were used, how the evaluation process was designed and implemented, and how these approaches can be adapted to multilingual domains.
£148.50
Springer Verlag, Singapore Machine Learning in Social Networks: Embedding Nodes, Edges, Communities, and Graphs
Book SynopsisThis book deals with network representation learning. It deals with embedding nodes, edges, subgraphs and graphs. There is a growing interest in understanding complex systems in different domains including health, education, agriculture and transportation. Such complex systems are analyzed by modeling, using networks that are aptly called complex networks. Networks are becoming ubiquitous as they can represent many real-world relational data, for instance, information networks, molecular structures, telecommunication networks and protein–protein interaction networks. Analysis of these networks provides advantages in many fields such as recommendation (recommending friends in a social network), biological field (deducing connections between proteins for treating new diseases) and community detection (grouping users of a social network according to their interests) by leveraging the latent information of networks. An active and important area of current interest is to come out with algorithms that learn features by embedding nodes or (sub)graphs into a vector space. These tasks come under the broad umbrella of representation learning. A representation learning model learns a mapping function that transforms the graphs' structure information to a low-/high-dimension vector space maintaining all the relevant properties. Table of ContentsIntroduction 1.1 introduction 1.2 Notations used in Book 1.3 Contents covered in this book 2 Representations of Networks 2.1 Introduction 2.2 Networks Represented as Graphs 2.3 Data Structures to Represent Graphs 2.3.1 Matrix Representation 2.3.2 Adjacency List 2.4 Network Embeddings 2.5 Evaluation Datasets 2.5.1 Evaluation Datasets 2.5.2 Evaluation Metrics 2.6 Machine Learning Downstream Tasks 2.6.1 Classification 2.6.2 Clustering 2.6.3 Link Prediction (LP) 2.6.4 Visualization 2.6.5 Network Reconstruction 2.7 Embeddings based on Matrix Factorization 2.7.1 Singular Value Decomposition (SVD) 2.7.2 Matrix Factorization based Clustering 2.7.3 Soft Clustering as Matrix Factorization 2.7.4 Non-negative Matrix factorization (NMF) 2.8 Word2vec 2.8.1 Skipgram model 2.9 Learning Network Embeddings 2.9.1 Supervised Learning 2.9.2 Unsupervised Learning 2.9.3 Node and Edge Embeddings 2.9.4 Graph Embedding 2.10 Summary 3 Deep Learning 3.1 Introduction 3.2 Neural Networks 3.2.1 Perceptron 3.2.2 Characteristics of Neural Networks 3.2.3 Multilayer Perceptron Networks 3.2.4 Training MLP Networks 3.3 Convolution Neural Networks 3.3.1 Activation Function 3.3.2 Initialization of Weights 3.3.3 Deep Feedforward Neural Network 3.4 Recurrent Networks 3.4.1 Recurrent Neural Networks 3.4.2 Long Short Term Memory 3.4.3 Different Gates used by LSTM 3.4.4 Training of LSTM Models 3.5 Learning Representations using Autoencoders 3.5.1 Types of Autoencoders 3.6 Summary References 4 Embedding Nodes and Edge 4.1 Introduction 4.2 Representation of Node and Edges as Vectors 4.3 Embeddings based on Random Walks 4.4 Embeddings based on Matrix Factorization 4.5 Graph Neural Network Models 4.6 State of the art algorithms 4.7 Evaluation methods and Machine Learning tasks 4.8 Summary References 5 Embedding Graphs 5.1 Introduction 5.2 Representation of Graphs as Vectors 5.3 Graph Representation using Node Embeddings 5.4 Graph Pooling Techniques 5.4.1 Global Pooling Methods 5.4.2 Hierarchical Pooling Methods 5.5 State of the art algorithms 5.6 Evaluation methods and Machine Learning tasks 5.7 Summary References
£49.49
World Scientific Publishing Co Pte Ltd Machine Learning Approaches To Bioinformatics
Book SynopsisThis book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research.Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes.An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects.Table of ContentsIntroduction to Unsupervised Learning; Probability Density Estimation and Applications in Bioinformatics; Dimension Reduction - Multidimensional Scaling and Principal Component Analysis, Cluster Analysis; Self-Organizing Map; Introduction to Supervised Learning; Classification and Regression Trees; Artificial Neural Networks; Vector Machines; Hidden Markov Models; Feature Selection in Bioinformatics; Biological Data Coding; Sequence/Structural Bioinformatics Foundation - Peptide Classification; Gene Network - Causal Network and Bayesian Networks; Metabolomics; S-Systems; Pathway Recognition; Future Directions; Appendices: R Codes; Study Data Sets.
£94.50
World Scientific Publishing Co Pte Ltd Gentle Introduction To Support Vector Machines In
Book SynopsisSupport Vector Machines (SVMs) are among the most important recent developments in pattern recognition and statistical machine learning. They have found a great range of applications in various fields including biology and medicine. However, biomedical researchers often experience difficulties grasping both the theory and applications of these important methods because of lack of technical background. The purpose of this book is to introduce SVMs and their extensions and allow biomedical researchers to understand and apply them in real-life research in a very easy manner. The book is to consist of two volumes: theory and methods (Volume 1) and case studies (Volume 2).Table of ContentsPreliminaries: Introduction and Book Overview; Methods Used in this Book; Case Studies and Comparative Evaluation in High-Throughput Genomic Data: Application and Comparison of SVMs and Other Methods for Multicategory Microarray-Based Cancer Classification; Comparison of SVMs and Random Forests for Microarray-Based Cancer Classification; Comparison of SVMs and Kernel Ridge Regression for Microarray-Based Cancer Classification (Contributed by Zhiguo Li); Application and Comparison of SVMs and Other Methods for Multicategory Classification in Microbiomics (Contributed by Mikael Henaff, Kranti Konganti, Varun Narendra, Alexander V Alekseyenko); Application to Assessment of Plasma Proteome Stability; Case Studies and Comparative Evaluation in Text Data: Application and Comparison of SVMs and Other Methods for Retrieving High-Quality Content-Specific Articles (Contributed by Yindalon Aphinyanaphongs); Application and Comparison of SVMs and Other Methods for Identifying Unproven Cancer Treatments on the Web (Contributed by Yindalon Aphinyanaphongs); Application to Predicting Future Article Citations (Contributed by Lawrence Fu); Application to Classifying Instrumentality of Article Citations (Contributed by Lawrence Fu); Application and Comparison of SVMs and Other Methods for Identifying Drug - Drug Interactions-Related Literature (Contributed by Stephany Duda); Case Studies with Clinical Data: Application to Predicting Clinical Laboratory Values; Application to Modeling Clinical Judgment and Guideline Compliance in the Diagnosis of Melanoma (Contributed by Andrea Sboner); Other Comparative Evaluation Studies of Broad Applicability: Using SVMs for Causal Variable Selection; Application and Comparison of SVM-RFE and GLL Methods.
£61.75
World Scientific Publishing Co Pte Ltd Quantitative Logic And Soft Computing -
Book SynopsisThe QL&SC 2012 is a major symposium for scientists, and practitioners all around the world to present their latest researches, results, ideas, developments and applications in such areas as quantitative logic, many-valued logic, fuzzy logic, quantification of software, artificial intelligence, fuzzy sets and systems and soft computing.This invaluable book provides a broad introduction to the fuzzy reasoning and soft computing. It is certain one should not go too far in approximation and optimization, and a certain degree must be kept in mind. This is the essential idea of quantitative logic and soft computing.The explanations in the book are complete to provide the necessary background material needed to go further into the subject and explore the research literature. It is suitable reading for graduate students. It provides a platform for mutual exchanges from top experts and scholars around the world in this field.Table of ContentsQuantitative Logic; Quantification of Software; Fuzzy Sets and Systems; Artificial Intelligence; Soft Computing; Non-Classical Automata Theory and Formal Languages.
£207.00
Springer Frontiers of Statistics and Data Science
Book SynopsisChapter 1: Artificial Intelligence in Precision Medicine and Digital Health.- Chapter 2: Revisiting Doob's Theorem on Posterior Consistency.- Chapter 3: The Central Limit Theorem in High-dimension.- Chapter 4: An Introduction to Deep Learning.- Chapter 5: The R Language and its Use in Statistics.- Chapter 6: Large Deviation Asymptotics for Systems with Fractional Noise.- Chapter 7: High dimensional Wigner matrices with general independent entries.- Chapter 8: Data Analysis after Record Linkage: Sources of Error, Consequences, and Possible Solutions.- Chapter 9: Statistical Inference of Network Data: Past, Present, and Future.- Chapter 10: Current topics in group testing.
£116.99
Springer Database Systems for Advanced Applications
Book SynopsisRecommendation.- Multi-media.
£128.88
Springer Database Systems for Advanced Applications
Book SynopsisMachine learning.- Text processing.
£62.99
Springer Machine Learning Applications in Renewable Energy
Book Synopsis
£116.99
World Scientific Publishing Co Pte Ltd Machine Learning With Python Theory And
Book Synopsis
£66.50
Springer Verlag, Singapore Hypergraph Computation
Book SynopsisThis open access book discusses the theory and methods of hypergraph computation. Many underlying relationships among data can be represented using graphs, for example in the areas including computer vision, molecular chemistry, molecular biology, etc. In the last decade, methods like graph-based learning and neural network methods have been developed to process such data, they are particularly suitable for handling relational learning tasks. In many real-world problems, however, relationships among the objects of our interest are more complex than pair-wise. Naively squeezing the complex relationships into pairwise ones will inevitably lead to loss of information which can be expected valuable for learning tasks. Hypergraph, as a generation of graph, has shown superior performance on modelling complex correlations compared with graph. Recent years have witnessed a great popularity of researches on hypergraph-related AI methods, which have been used in computer vision, social media analysis, etc. We summarize these attempts as a new computing paradigm, called hypergraph computation, which is to formulate the high-order correlations underneath the data using hypergraph, and then conduct semantic computing on the hypergraph for different applications. The content of this book consists of hypergraph computation paradigms, hypergraph modelling, hypergraph structure evolution, hypergraph neural networks, and applications of hypergraph computation in different fields. We further summarize recent achievements and future directions on hypergraph computation in this book.Table of Contents
£38.52
Springer Verlag, Singapore Neural Text-to-Speech Synthesis
Book SynopsisText-to-speech (TTS) aims to synthesize intelligible and natural speech based on the given text. It is a hot topic in language, speech, and machine learning research and has broad applications in industry. This book introduces neural network-based TTS in the era of deep learning, aiming to provide a good understanding of neural TTS, current research and applications, and the future research trend. This book first introduces the history of TTS technologies and overviews neural TTS, and provides preliminary knowledge on language and speech processing, neural networks and deep learning, and deep generative models. It then introduces neural TTS from the perspective of key components (text analyses, acoustic models, vocoders, and end-to-end models) and advanced topics (expressive and controllable, robust, model-efficient, and data-efficient TTS). It also points some future research directions and collects some resources related to TTS. This book is the first to introduce neural TTS in a comprehensive and easy-to-understand way and can serve both academic researchers and industry practitioners working on TTS.Table of Contents
£107.99
Springer Verlag, Singapore Machine Learning Contests: A Guidebook
Book SynopsisThis book systematically introduces the competitions in the field of algorithm and machine learning. The first author of the book has won 5 championships and 5 runner-ups in domestic and international algorithm competitions.Firstly, it takes common competition scenarios as a guide by giving the main processes of using machine learning to solve real-world problems, namely problem modelling, data exploration, feature engineering, model training. And then lists the main points of difficulties, general ideas with solutions in the whole process. Moreover, this book comprehensively covers several common problems in the field of machine learning competitions such as recommendation, temporal prediction, advertising, text computing, etc. The authors, also knew as "competition professionals”, will explain the actual cases in detail and teach you various processes, routines, techniques and strategies, which is a rare treasure book for all competition enthusiasts. It is very suitable for readers who are interested in algorithm competitions and deep learning algorithms in practice, or computer-related majors.Table of ContentsChapter 1 First Sight.- Chapter 2 Problem Modeling.- Chapter 3 Data Exploration.- Chapter 4 Characteristic Engineering.- Chapter 5 Model Training .- Chapter 6 Model Fusion.- Chapter 7 User Portrait.- Chapter 8 Actual Combat Case: Elo Merchant.- Chapter 9 time sequence.- Chapter 10 Practical Cases: Global Urban.- Chapter 11 Practical Case: Corporaci .-Corporación Favorita Grocery Sales Forecasting.- Chapter 12 Computing Advertising.- Chapter 13 Practical Cases: Tencent 2018 Advertising Algorithm Contest-Similarity Crowd Expansion.- Chapter 14: TalkingData AdTracking Fraud Detection Challenge.- Chapter 15 Natural Language Processing.- Chapter 16 Practical Case: Quora Question Pairs.
£44.99
Springer Verlag, Singapore WAIC and WBIC with Python Stan: 100 Exercises for
Book SynopsisMaster the art of machine learning and data science by diving into the essence of mathematical logic with this comprehensive textbook. This book focuses on the widely applicable information criterion (WAIC), also described as the Watanabe-Akaike information criterion, and the widely applicable Bayesian information criterion (WBIC), also described as the Watanabe Bayesian information criterion. The book expertly guides you through relevant mathematical problems while also providing hands-on experience with programming in Python and Stan. Whether you’re a data scientist looking to refine your model selection process or a researcher who wants to explore the latest developments in Bayesian statistics, this accessible guide will give you a firm grasp of Watanabe Bayesian Theory.The key features of this indispensable book include: A clear and self-contained writing style, ensuring ease of understanding for readers at various levels of expertise. 100 carefully selected exercises accompanied by solutions in the main text, enabling readers to effectively gauge their progress and comprehension. A comprehensive guide to Sumio Watanabe’s groundbreaking Bayes theory, demystifying a subject once considered too challenging even for seasoned statisticians. Detailed source programs and Stan codes that will enhance readers’ grasp of the mathematical concepts presented. A streamlined approach to algebraic geometry topics in Chapter 6, making Bayes theory more accessible and less daunting. Embark on your machine learning and data science journey with this essential textbook and unlock the full potential of WAIC and WBIC today!Table of ContentsOver view of Watanabe's Bayes.- Introduction to Watanabe Bayesian Theory.- MCMC and Stan.- Mathematical Preparation.- Regular Statistical Models.- Information Criteria.- Algebraic Geometry.- The Essence of WAOIC.- WBIC and Its Application to Machine Learning.
£40.49
Springer Verlag, Singapore Machine Learning Methods
Book SynopsisThis book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning. Table of ContentsChapter 1 Introduction to Machine learning and Supervised Learning.- Chapter 2 Perceptron.- Chapter 3 K-Nearest-Neighbor.- Chapter 4 The Naïve Bayes Method.- Chapter 5 Decision Tree.- Chapter 6 Logistic Regression and Maximum Entropy Model.- Chapter 7 Support Vector Machine.- Chapter 8 Boosting.- Chapter 9 EM Algorithm and Its Extensions.- Chapter 10 Hidden Markov Model.- Chapter 11 Conditional Random Field.
£71.99
Springer Verlag, Singapore Artificial Intelligence in Business Management
Book SynopsisArtificial intelligence (AI) is rapidly gaining significance in the business world. With more and more organizations adopt AI technologies, there is a growing demand for business leaders, managers, and practitioners who can harness AI’s potential to improve operations, increase efficiency, and drive innovation. This book aims to help management professionals exploit the predictive powers of AI and demonstrate to AI practitioners how to apply their expertise in fundamental business operations. It showcases how AI technology innovations can enhance various aspects of business management, such as business strategy, finance, and marketing. Readers interested in AI for business management will find several topics of particular interest, including how AI can improve decision-making in business strategy, streamline operational processes, and enhance customer satisfaction. As AI becomes an increasingly important tool in the business world, this book offers valuable insights into how it can be applied to various industries and business settings. Through this book, readers will gain a better understanding of how AI can be applied to improve business management practices and practical guidance on how to implement AI projects in a business context. This book also provides practical guides on how to implement AI projects in a business context using Python programming. By reading this book, readers will be better equipped to make informed decisions about how to leverage AI for business success.Table of ContentsPart I: Artificial Intelligence Algorithms.- Chapter 1. Introduction to Artificial Intelligence.- Chapter 2. Regression.- Chapter 3. Classification.- Chapter 4. Clustering.- Chapter 5. Time Series.- Chapter 6. Convolutional Neural Networks.- Chapter 7. Text Mining.- Chapter 8. Chatbot, Speech and NLP.- Part II: Applications of Artificial Intelligence in Business Management.- Chapter 9. AI in Human Resource Management.- Chapter 10. AI in Sales.- Chapter 11. AI in Marketing.- Chapter 12. AI in Supply Chain Management.- Chapter 13. AI in Operations Management.- Chapter 14. AI in Corporate Finance.- Chapter 15. AI in Business Law.- Chapter 16. AI in Business Strategy.- References.- Index.
£67.49
Springer Verlag, Singapore Statistics and Data Analysis for Engineers and
Book SynopsisThis textbook summarizes the different statistical, scientific, and financial data analysis methods for users ranging from a high school level to a professional level. It aims to combine the data analysis methods using three different programs—Microsoft Excel, SPSS, and MATLAB. The book combining the different data analysis tools is a unique approach. The book presents a variety of real-life problems in data analysis and machine learning, delivering the best solution. Analysis methods presented in this book include but are not limited to, performing various algebraic and trigonometric operations, regression modeling, and correlation, as well as plotting graphs and charts to represent the results. Fundamental concepts of applied statistics are also explained here, with illustrative examples. Thus, this book presents a pioneering solution to help a wide range of students, researchers, and professionals learn data processing, interpret different findings derived from the analyses, and apply them to their research or professional fields. The book also includes worked examples of practical problems. The primary focus behind designing these examples is understanding the concepts of data analysis and how it can solve problems. The chapters include practice exercises to assist users in enhancing their skills to execute statistical analysis calculations using software instead of relying on tables for probabilities and percentiles in the present world. Table of ContentsGeneral Overview.- Introduction to MATLAB.- The Fundamentals of Microsoft Excel.- Introduction to SPSS.
£61.74
Samurai Media Limited Amazon SageMaker Developer Guide
Book Synopsis
£66.49
Romy Group LLC Bots in Suits: Using Generative AI to
Book Synopsis
£11.24
Association of Computing Machinery,U.S. From Algorithms to Thinking Machines: The New
Book SynopsisThis book introduces and provides an analysis of the basic concepts of algorithms, data, and computation and discusses the role of algorithms in ruling and shaping our world. It provides a clear understanding of the power and impact on humanity of the pervasive use of algorithms.From Algorithms to Thinking Machines combines a layman's approach with a well-founded scientific description to discuss both principles and applications of algorithms, Big Data, and machine intelligence. The book provides a clear and deep description of algorithms, software systems, data-driven applications, machine learning, and data science concepts, as well as the evolution and impact of artificial intelligence.After introducing computing concepts, the book examines the relationships between algorithms and human work, discussing how jobs are being affected and how computers and software programs are influencing human life and the labor sphere. Topics such as value alignment, collective intelligence, Big Data impact, automatic decision methods, social control, and political uses of algorithms are illustrated and discussed at length without excessive technical detail. Issues related to how corporations, governments, and autocratic regimes are exploiting algorithms and machine intelligence methods to influence people, laws, and markets are extensively addressed. Ethics principles in software programming and human value insertion into artificial intelligence algorithms are also discussed.
£46.80
Association of Computing Machinery,U.S. The Societal Impacts of Algorithmic
Book SynopsisThis book demonstrates the need for and the value of interdisciplinary research in addressing important societal challenges associated with the widespread use of algorithmic decision-making. Algorithms are increasingly being used to make decisions in various domains such as criminal justice, medicine, and employment. While algorithmic tools have the potential to make decision-making more accurate, consistent, and transparent, they pose serious challenges to societal interests. For example, they can perpetuate discrimination, cause representational harm, and deny opportunities.The Societal Impacts of Algorithmic Decision-Making presents several contributions to the growing body of literature that seeks to respond to these challenges, drawing on techniques and insights from computer science, economics, and law. The author develops tools and frameworks to characterize the impacts of decision-making and incorporates models of behavior to reason about decision-making in complex environments. These technical insights are leveraged to deepen the qualitative understanding of the impacts of algorithms on problem domains including employment and lending.The social harms of algorithmic decision-making are far from being solved. While easy solutions are not presented here, there are actionable insights for those who seek to deploy algorithms responsibly. The research presented within this book will hopefully contribute to broader efforts to safeguard societal values while still taking advantage of the promise of algorithmic decision-making.Table of Contents Introduction Part I: Theoretical Foundations for Fairness in Algorithmic Decision-Making 1. Inherent Tradeoffs in the Fair Determination of Risk Scores 2. On Fairness and Calibration 3. The Externalities of Exploration and How Data Diversity Helps Exploitation Part II: Models of Behavior 4. Selection Problems in the Presence of Implicit Bias 5. How Do Classifiers Induce Agents to Behave Strategically? 6. Algorithmic Monoculture and Social Welfare Part III: Application Domains 7. Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices 8. The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons Part IV: Conclusion and Future Work 9. Future Directions
£54.00
Association for Computing Machinery Formal Methods for Safe Autonomy
£40.84
Association for Computing Machinery Formal Methods for Safe Autonomy
£55.09
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 Generative AI with SAP and Amazon Bedrock
Book SynopsisExplore Generative AI and understand its key concepts, architecture, and tangible business use cases. This book will help you develop the skills needed to use SAP AI Core service features available in the SAP Business Technology Platform. You'll examine large language model (LLM) concepts and gain the practical knowledge to unleash the best use of Gen AI. As you progress, you'll learn how to get started with your own LLM models and work with Generative AI use cases. Additionally, you'll see how to take advantage Amazon Bedrock stack using AWS SDK for ABAP. To fully leverage your knowledge, Generative AI with SAP and Amazon Bedrock offers practical step-by-step instructions for how to establish a cloud SAP BTP account model and create your first GenAIartifacts. This work is an important prerequisite for those who want to take full advantage of generative AI with SAP. What You Will LearnMaster the concepts and terminology of artificial intelligence and GenAIUnderstand opportunities and impacts for different industries with GenAIBecome familiar with SAP AI Core, Amazon Bedrock, AWS SDK for ABAP and develop your firsts GenAI projectsAccelerate your development skillsGain more productivity and time implementing GenAI use casesWho this Book Is ForAnyone who wants to learn about Generative AI for Enterprise and SAP practitioners who want to take advantage of AI within the SAP ecosystem to support their systems and workflows.
£35.99
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 Neural Networks with TensorFlow and Keras
Book SynopsisChapter 1: Introduction to Neural Networks.- Chapter 2: Using Tensors.- Chapter 3: How Machines Learn.- Chapter 4: Network Layers.- Chapter 5: The Training Process.- Chapter 6: Generative Models.- Chapter 7: Re-enforcement Learning.- Chapter 8: Using Pre-trained Networks.
£35.99
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.
£28.79
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
