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Theory of Probability
Another title in the reissued Oxford Classic Texts in the Physical Sciences series, Jeffrey's Theory of Probability, first published in 1939, was the first to develop a fundamental theory of scientific inference based on the ideas of Bayesian statistics. His ideas were way ahead of their time and it is only in the past ten years that the subject of Bayes' factors has been significantly developed and extended. Until recently the two schoolsof statistics (Bayesian and Frequentist) were distinctly different and set apart. Recent work (aided by increased computer power and availability) has changed all that and today's graduate students and researchers all require an understanding of Bayesian ideas. This book is their starting point.
R 5,221.00
Hands-On Reinforcement Learning for Games
Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlowKey FeaturesGet to grips with the different reinforcement and DRL algorithms for game developmentLearn how to implement components such as artificial agents, map and level generation, and audio generationGain insights into cutting-edge RL research and understand how it is similar to artificial general researchBook DescriptionWith the increased presence of AI in the gaming industry, developers are challenged to create highly responsive and adaptive games by integrating artificial intelligence into their projects. This book is your guide to learning how various reinforcement learning techniques and algorithms play an important role in game development with Python.Starting with the basics, this book will help you build a strong foundation in reinforcement learning for game development. Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. Learning these techniques will enhance your game development skills and add a variety of features to improve your game agent’s productivity. As you advance, you’ll understand how deep reinforcement learning (DRL) techniques can be used to devise strategies to help agents learn from their actions and build engaging games.By the end of this book, you’ll be ready to apply reinforcement learning techniques to build a variety of projects and contribute to open source applications.What you will learnUnderstand how deep learning can be integrated into an RL agentExplore basic to advanced algorithms commonly used in game developmentBuild agents that can learn and solve problems in all types of environmentsTrain a Deep Q-Network (DQN) agent to solve the CartPole balancing problemDevelop game AI agents by understanding the mechanism behind complex AIIntegrate all the concepts learned into new projects or gaming agentsWho this book is forIf you’re a game developer looking to implement AI techniques to build next-generation games from scratch, this book is for you. Machine learning and deep learning practitioners, and RL researchers who want to understand how to use self-learning agents in the game domain will also find this book useful. Knowledge of game development and Python programming experience are required.
R 1,498.00
Hands-On Deep Learning Architectures with Python
Concepts, tools, and techniques to explore deep learning architectures and methodologiesKey FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook DescriptionDeep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations.By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is forIf you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book
R 1,140.00
Deep Learning for Medical Image Analysis
Deep learning is providing exciting solutions for medical image analysis problems and is seen as a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component have been applied to medical image detection, segmentation and registration, and computer-aided analysis, using a wide variety of application areas. Deep Learning for Medical Image Analysis is a great learning resource for academic and industry researchers in medical imaging analysis, and for graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis.
R 3,678.00
Effective Statistical Learning Methods for Actuaries III
This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible.Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting.Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning.This is the third of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.
R 2,198.00
Deep Learning Innovations and Their Convergence With Big Data
The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics.Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.Contents include:Deep Auto-EncodersDeep Neural NetworkDomain Adaptation ModelingMultilayer Perceptron (MLP)Natural Language Processing (NLP)Restricted Boltzmann Machines (RBM)Threat Detection
R 7,931.55
Probability and Statistics in Experimental Physics
Second edition includes a new chapter on queuing problems and introduces a new method for dealing with experiments in which only a few events are observed.
R 1,551.22
Probability Models And Applications (Revised Second Edition)
Written by renowned experts in the field, this reissue of a textbook has as its unifying theme the role that probability models have had, and continue to have, in scientific and practical applications. It includes many examples, with actual data, of real-world use of probability models, while expositing the mathematical theory of probability at an introductory calculus-based level. Detailed descriptions of the properties and applications of probability models that have successfully modeled real phenomena are given, as well as an explanation of methods for testing goodness of fit of these models. Readers will receive a firm foundation in techniques for deriving distributions of various summaries of data that will prepare them for subsequent studies of statistics, as well as a solid grounding in concepts such as that of conditional probability that will prepare them for more advanced courses in stochastic processes.
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Applied Probability and Statistics
This book moves systematically through the topic of applied probability from an introductory chapter to such topics as random variables and vectors, stochastic processes, estimation, testing and regression. The topics are well chosen and the presentation is enriched by many examples from real life. Each chapter concludes with many original, solved and unsolved problems and hundreds of multiple choice questions, enabling those unfamiliar with the topics to master them. Additionally appealing are historical notes on the mathematicians mentioned throughout, and a useful bibliography. A distinguishing character of the book is its thorough and succinct handling of the varied topics.
R 1,837.51
Deep Learning on Embedded Systems
Comprehensive, accessible introduction to deep learning for engineering tasks through Python programming, low-cost hardware, and freely available software Deep Learning on Embedded Systems is a comprehensive guide to the practical implementation of deep learning for engineering tasks through computers and embedded hardware such as Raspberry Pi and Nvidia Jetson Nano. After an introduction to the field, the book provides fundamental knowledge on deep learning, convolutional and recurrent neural networks, computer vision, and basics of Linux terminal and docker engines. This book shows detailed setup steps of Jetson Nano and Raspberry Pi for utilizing essential frameworks such as PyTorch and OpenCV. GPU configuration and dependency installation procedure for using PyTorch is also discussed allowing newcomers to seamlessly navigate the learning curve. A key challenge of utilizing deep learning on embedded systems is managing limited GPU and memory resources. This book outlines a strategy of training complex models on a desktop computer and transferring them to embedded systems for inference. Also, students and researchers often face difficulties with the varying probabilistic theories and notations found in data science literature. To simplify this, the book mainly focuses on the practical implementation part of deep learning using Python programming, low-cost hardware, and freely available software such as Anaconda and Visual Studio Code. To aid in reader learning, questions and answers are included at the end of most chapters. Written by a highly qualified author, Deep Learning on Embedded Systems includes discussion on: Fundamentals of deep learning, including neurons and layers, activation functions, network architectures, hyperparameter tuning, and convolutional and recurrent neural networks (CNNs & RNNs)PyTorch, OpenCV, and other essential framework setups for deep transfer learning, along with Linux terminal operations, docker engine, docker images, and virtual environments in embedded devicesTraining models for image classification and object detection with classification, then converting trained PyTorch models to ONNX format for efficient deployment on Jetson Nano and Raspberry Pi Deep Learning on Embedded Systems serves as an excellent introduction to the field for undergraduate engineering students seeking to learn deep learning implementations for their senior capstone or class projects and graduate researchers and educators who wish to implement deep learning in their research.
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Machine Learning Algorithms for Signal and Image Processing
Machine Learning Algorithms for Signal and Image Processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systems, and green energy How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.
R 5,210.00
Deep Learning in Computer Vision
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.
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Digital Twins
This book centres on the topic of digital twins for superior healthcare decision support, as access is enabled to large volumes of multi-dimensional data such as patient’s electronic medical records, medical scans, and data. The reader learns about the possibility of a digital representation of analogous clinical cases built from data-driven models to represent and present relevant information and germane knowledge in context.Together with cutting-edge technologies, the authors share the ability of data-driven models to offer more efficient clinical decision support. The authors take a three-prong approach in the study of digital twins, the positive contributions made in other industries, the different types of applications and the numerous benefits offered. Artificial intelligence (AI) techniques, such as machine learning (ML) and deep learning (DL) algorithms, are discussed in the context of digital twins in healthcare applications. By looking at digital twins it is possible to reduce workflow challenges and provide fast and precise diagnosis. This then demonstrates how digital twins therefore support superior clinical decision-making. Importantly, the authors identify critical success issues, including co-design and research, for the design, development, and deployment of suitable digital twins.This book is written for the healthcare audience, professionals, physicians, medical administrators, managers, and IT practitioners. It also serves as a useful reference for senior-level undergraduate students and graduate students in health informatics and public health.
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Probability: Theory, Examples, Problems, Simulations
A key pedagogical feature of the textbook is the accessible approach to probability concepts through examples with explanations and problems with solutions. The reader is encouraged to simulate in Matlab random experiments and to explore the theoretical aspects of the probabilistic models behind the studied experiments. By this appropriate balance between simulations and rigorous mathematical approach, the reader can experience the excitement of comprehending basic concepts and can develop the intuitive thinking in solving problems. The current textbook does not contain proofs for the stated theorems, but corresponding references are given. Moreover, the given Matlab codes and detailed solutions make the textbook accessible to researchers and undergraduate students, by learning various techniques from probability theory and its applications in other fields. This book is intended not only for students of mathematics but also for students of natural sciences, engineering, computer science and for science researchers, who possess the basic knowledge of calculus for the mathematical concepts of the textbook and elementary programming skills for the Matlab simulations.
R 2,184.47
Convergence of Probability Measures
A new look at weak-convergence methods in metric spaces-from a master of probability theory In this new edition, Patrick Billingsley updates his classic work Convergence of Probability Measures to reflect developments of the past thirty years. Widely known for his straightforward approach and reader-friendly style, Dr. Billingsley presents a clear, precise, up-to-date account of probability limit theory in metric spaces. He incorporates many examples and applications that illustrate the power and utility of this theory in a range of disciplines-from analysis and number theory to statistics, engineering, economics, and population biology. With an emphasis on the simplicity of the mathematics and smooth transitions between topics, the Second Edition boasts major revisions of the sections on dependent random variables as well as new sections on relative measure, on lacunary trigonometric series, and on the Poisson-Dirichlet distribution as a description of the long cycles in permutations and the large divisors of integers. Assuming only standard measure-theoretic probability and metric-space topology, Convergence of Probability Measures provides statisticians and mathematicians with basic tools of probability theory as well as a springboard to the "industrial-strength" literature available today.
R 6,674.00
Artificial Intelligence and Deep Learning for Computer Network
Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis aims to systematically collect quality research spanning AI, ML, and deep learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights on the applicability of the theoretical similitudes, otherwise a rarity in many such books.Features: A diverse collection of important and cutting-edge topics covered in a single volume. Several chapters on cybersecurity, an extremely active research area. Recent research results from leading researchers and some pointers to future advancements in methodology. Detailed experimental results obtained from standard data sets.This book serves as a valuable reference book for students, researchers, and practitioners who wish to study and get acquainted with the application of cutting-edge AI, ML, and DL techniques to network management and cyber security.
R 6,646.09
Basic Discrete Mathematics: Logic, Set Theory, And Probability
This lively introductory text exposes the student in the humanities to the world of discrete mathematics. A problem-solving based approach grounded in the ideas of George Pólya are at the heart of this book. Students learn to handle and solve new problems on their own. A straightforward, clear writing style and well-crafted examples with diagrams invite the students to develop into precise and critical thinkers. Particular attention has been given to the material that some students find challenging, such as proofs. This book illustrates how to spot invalid arguments, to enumerate possibilities, and to construct probabilities. It also presents case studies to students about the possible detrimental effects of ignoring these basic principles. The book is invaluable for a discrete and finite mathematics course at the freshman undergraduate level or for self-study since there are full solutions to the exercises in an appendix.'Written with clarity, humor and relevant real-world examples, Basic Discrete Mathematics is a wonderful introduction to discrete mathematical reasoning.'- Arthur Benjamin, Professor of Mathematics at Harvey Mudd College, and author of The Magic of Math
R 4,838.59
Big Data Analysis and Deep Learning Applications
This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.
R 7,326.00
Multivariate Probability
This book is a comprehensive guide to multivariate probability for students who have an elementary knowledge of probability and are ready to move on to more advanced concepts Topics covered include: A review of basic probability theory, including core ideas about random variables Bivariate distributions and the general theory of random vectors Relationships between random variables Normal linear model and multivariate sampling distributions Generating functions and convergence. Each section is illustrated with numerous examples. Multivariate probability deliberately avoids a measure-theoretic approach in order to make these complex concepts easily accessible to a broad readership. Attention is restricted to discrete and (absolutely) continuous random variables. Although proofs are given of all the main results, this book is primarily intended to provide readers with the tools they require to build appropriate probability models for real-life situations. The usefulness of simulation in this respect is emphasized throughout the book.
R 1,627.00
Fundamentals of Probability and Stochastic Processes with Applications to Communications
This book provides engineers with focused treatment of the mathematics needed to understand probability, random variables, and stochastic processes, which are essential mathematical disciplines used in communications engineering. The author explains the basic concepts of these topics as plainly as possible so that people with no in-depth knowledge of these mathematical topics can better appreciate their applications in real problems. Applications examples are drawn from various areas of communications. If a reader is interested in understanding probability and stochastic processes that are specifically important for communications networks and systems, this book serves his/her need.
R 3,574.95
Machine Learning, Deep Learning, and Blockchain
This book constitutes the refereed proceedings of Fourth International Research Conference on Machine Learning, Deep Learning, and Blockchain, IRCICD 2023, held in Chennai, India, during April 28–29, 2023. The 9 full papers presented were carefully reviewed and selected from 147 submissions. They were organized in topical sections as follows: machine learning; deep learning; and block chain.
R 4,626.82
Probability Models And Applications (Revised Second Edition)
Written by renowned experts in the field, this reissue of a textbook has as its unifying theme the role that probability models have had, and continue to have, in scientific and practical applications. It includes many examples, with actual data, of real-world use of probability models, while expositing the mathematical theory of probability at an introductory calculus-based level. Detailed descriptions of the properties and applications of probability models that have successfully modeled real phenomena are given, as well as an explanation of methods for testing goodness of fit of these models. Readers will receive a firm foundation in techniques for deriving distributions of various summaries of data that will prepare them for subsequent studies of statistics, as well as a solid grounding in concepts such as that of conditional probability that will prepare them for more advanced courses in stochastic processes.
R 2,883.96
International Conference on Deep Learning, Big Data and Blockchain (Deep-BDB 2021)
The role of deep learning for the analysis and learning of massive amounts of data from all aspects of daily-life has dramatically changed over the last few years. It is increasingly helping uncover trends leading to great successes. This book includes a collection of research manuscripts presenting state-of-the-art work in the areas of deep learning, blockchain and big data. All the manuscripts included in this book have been peer-reviewed based on aspects of novelty, originality and rigour. The main topics covered in the book include machine learning and time series, blockchain technologies and applications, data security, deep learning, and Internet of Things.
R 8,301.34
Deep Learning for Cancer Diagnosis
This book explores various applications of deep learning to the diagnosis of cancer,while also outlining the future face of deep learning-assisted cancer diagnostics. As is commonly known, artificial intelligence has paved the way for countless new solutions in the field of medicine. In this context, deep learning is a recent and remarkable sub-field, which can effectively cope with huge amounts of data and deliver more accurate results. As a vital research area, medical diagnosis is among those in which deep learning-oriented solutions are often employed.Accordingly, the objective of this book is to highlight recent advanced applications of deep learning for diagnosing different types of cancer. The target audience includes scientists, experts, MSc and PhD students, postdocs, and anyone interested in the subjects discussed. The book can be used as a reference work to support courses on artificial intelligence, medical and biomedicaleducation.
R 6,865.95
Graph Neural Networks: Essentials and Use Cases
This book explains the technologies and tools that underpin GNNs, offering a clear and practical guide to their industrial applications and use cases. AI engineers, data scientists, and researchers in AI and graph theory will find detailed insights into the latest trends and innovations driving this dynamic field. With practical chapters demonstrating how GNNs are reshaping various industry verticals--and how they complement advances in generative, agentic, and physical AI--this book is an essential resource for understanding and leveraging their potential. The neural network paradigm has surged in popularity for its ability to uncover hidden patterns within vast datasets. This transformative technology has spurred global innovations, particularly through the evolution of deep neural networks (DNNs). Convolutional neural networks (CNNs) have revolutionized computer vision, while recurrent neural networks (RNNs) and their advanced variants have automated natural language processing tasks such as speech recognition, translation, and content generation. Traditional DNNs primarily handle Euclidean data, yet many real-world problems involve non-Euclidean data--complex relationships and interactions naturally represented as graphs. This challenge has driven the rise of graph neural networks (GNNs), an approach that extends deep learning into new domains. GNNs are powerful models designed to work with graph-structured data, where nodes represent individual data points and edges denote the relationships between them. Several variants have emerged: Graph Convolutional Networks (GCNs): These networks learn from a node's local neighborhood by aggregating information from adjacent nodes, updating the node's representation in the process.Graph Attentional Networks (GATs): By incorporating attention mechanisms, GATs focus on the most relevant neighbors during aggregation, enhancing model performance.Graph Recurrent Networks (GRNs): These networks combine principles from RNNs with graph structures to capture dynamic relationships within the data. GNNs are applied in a variety of advanced use cases, including node classification, link prediction, graph clustering, anomaly detection, recommendation systems, and also in natural language processing and computer vision. They help forecast traffic patterns, analyze molecular structures, verify programs, predict social influence, model electronic health records, and map brain networks.
R 8,661.69
Elementary Introduction to Statistical Learning Theory
A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind contemporary machine learning and uniquely utilize its foundations as a framework for philosophical thinking about inductive inference. Promoting the fundamental goal of statistical learning, knowing what is achievable and what is not, this book demonstrates the value of a systematic methodology when used along with the needed techniques for evaluating the performance of a learning system. First, an introduction to machine learning is presented that includes brief discussions of applications such as image recognition, speech recognition, medical diagnostics, and statistical arbitrage. To enhance accessibility, two chapters on relevant aspects of probability theory are provided. Subsequent chapters feature coverage of topics such as the pattern recognition problem, optimal Bayes decision rule, the nearest neighbor rule, kernel rules, neural networks, support vector machines, and boosting. Appendices throughout the book explore the relationship between the discussed material and related topics from mathematics, philosophy, psychology, and statistics, drawing insightful connections between problems in these areas and statistical learning theory. All chapters conclude with a summary section, a set of practice questions, and a reference sections that supplies historical notes and additional resources for further study. An Elementary Introduction to Statistical Learning Theory is an excellent book for courses on statistical learning theory, pattern recognition, and machine learning at the upper-undergraduate and graduate levels. It also serves as an introductory reference for researchers and practitioners in the fields of engineering, computer science, philosophy, and cognitive science that would like to further their knowledge of the topic.
R 4,264.00
Probability And Stochastic Processes: Work Examples
This volume offers a comprehensive collection of exercises, problems, and fully worked solutions covering the core topics of a first course in probability. Each chapter opens with a concise overview of essential probabilistic tools — such as distributions, transforms, martingales, and Gaussian processes — followed by examples that illustrate key techniques and applications.This substantially revised second edition introduces numerous new exercises, enhances the treatment of discrete and continuous distributions, and includes strengthened sections on martingale inequalities and Gaussian processes. Ideal for building intuition and confidence, the book provides clear reasoning, rigorous solutions, and broad coverage of classical and modern topics in probability and stochastic processes.
R 2,491.00
Probability and Forensic Evidence
This book addresses the role of statistics and probability in the evaluation of forensic evidence, including both theoretical issues and applications in legal contexts. It discusses what evidence is and how it can be quantified, how it should be understood, and how it is applied (and, sometimes, misapplied). After laying out their philosophical position, the authors begin with a detailed study of the likelihood ratio. Following this grounding, they discuss applications of the likelihood ratio to forensic questions, in the abstract and in concrete cases. The analysis of DNA evidence in particular is treated in great detail. Later chapters concern Bayesian networks, frequentist approaches to evidence, the use of belief functions, and the thorny subject of database searches and familial searching. Finally, the authors provide commentary on various recommendation reports for forensic science. Written to be accessible to a wide audience of applied mathematicians, forensic scientists, and scientifically-oriented legal scholars, this book is a must-read for all those interested in the mathematical and philosophical foundations of evidence and belief.
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Applications of Machine Learning and Deep Learning on Biological Data
The automated learning of machines characterizes machine learning (ML). It focuses on making data-driven predictions using programmed algorithms. ML has several applications, including bioinformatics, which is a discipline of study and practice that deals with applying computational derivations to obtain biological data. It involves the collection, retrieval, storage, manipulation, and modeling of data for analysis or prediction made using customized software. Previously, comprehensive programming of bioinformatical algorithms was an extremely laborious task for such applications as predicting protein structures. Now, algorithms using ML and deep learning (DL) have increased the speed and efficacy of programming such algorithms.Applications of Machine Learning and Deep Learning on Biological Data is an examination of applying ML and DL to such areas as proteomics, genomics, microarrays, text mining, and systems biology. The key objective is to cover ML applications to biological science problems, focusing on problems related to bioinformatics. The book looks at cutting-edge research topics and methodologies in ML applied to the rapidly advancing discipline of bioinformatics. ML and DL applied to biological and neuroimaging data can open new frontiers for biomedical engineering, such as refining the understanding of complex diseases, including cancer and neurodegenerative and psychiatric disorders. Advances in this field could eventually lead to the development of precision medicine and automated diagnostic tools capable of tailoring medical treatments to individual lifestyles, variability, and the environment.Highlights include:Artificial Intelligence in treating and diagnosing schizophreniaAn analysis of ML’s and DL’s financial effect on healthcareAn XGBoost-based classification method for breast cancer classificationUsing ML to predict squamous diseasesML and DL applications in genomics and proteomicsApplying ML and DL to biological data
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Analytic Methods of Probability Theory
No detailed description available for "Analytic Methods of Probability Theory".
R 6,071.00
Advances on Methodological and Applied Aspects of Probability and Statistics
This is one of two volumes that sets forth invited papers presented at the International Indian Statistical Association Conference. This volume emphasizes advancements in methodology and applications of probability and statistics. The chapters, representing the ideas of vanguard researchers on the topic, present several different subspecialties, including applied probability, models and applications, estimation and testing, robust inference, regression and design and sample size methodology. The text also fully describes the applications of these new ideas to industry, ecology, biology, health, economics and management. Researchers and graduate students in mathematical analysis, as well as probability and statistics professionals in industry, will learn much from this volume.
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Probability and Bayesian Statistics
This book contains selected and refereed contributions to the "Inter national Symposium on Probability and Bayesian Statistics" which was orga nized to celebrate the 80th birthday of Professor Bruno de Finetti at his birthplace Innsbruck in Austria. Since Professor de Finetti died in 1985 the symposium was dedicated to the memory of Bruno de Finetti and took place at Igls near Innsbruck from 23 to 26 September 1986. Some of the pa pers are published especially by the relationship to Bruno de Finetti's scientific work. The evolution of stochastics shows growing importance of probability as coherent assessment of numerical values as degrees of believe in certain events. This is the basis for Bayesian inference in the sense of modern statistics. The contributions in this volume cover a broad spectrum ranging from foundations of probability across psychological aspects of formulating sub jective probability statements, abstract measure theoretical considerations, contributions to theoretical statistics and stochastic processes, to real applications in economics, reliability and hydrology. Also the question is raised if it is necessary to develop new techniques to model and analyze fuzzy observations in samples. The articles are arranged in alphabetical order according to the family name of the first author of each paper to avoid a hierarchical ordering of importance of the different topics. Readers interested in special topics can use the index at the end of the book as guide.
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Machine Learning-Augmented Spectroscopies for Intelligent Materials Design
The thesis contains several pioneering results at the intersection of state-of-the-art materials characterization techniques and machine learning. The use of machine learning empowers the information extraction capability of neutron and photon spectroscopies. In particular, new knowledge and new physics insights to aid spectroscopic analysis may hold great promise for next-generation quantum technology. As a prominent example, the so-called proximity effect at topological material interfaces promises to enable spintronics without energy dissipation and quantum computing with fault tolerance, yet the characteristic spectral features to identify the proximity effect have long been elusive. The work presented within permits a fine resolution of its spectroscopic features and a determination of the proximity effect which could aid further experiments with improved interpretability. A few novel machine learning architectures are proposed in this thesis work which leverage the case when the data is scarce and utilize the internal symmetry of the system to improve the training quality. The work sheds light on future pathways to apply machine learning to augment experiments.
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Development and Analysis of Deep Learning Architectures
This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
R 7,592.74
Introduction to Probability and Random Variables
This textbook provides a straightforward, clear explanation of probability and random variables for communications engineering students. The author focuses on the most essential subjects of probability and random variables, eliminating unnecessary details of this difficult subject. After an introduction to the topic, the author covers the essentials of experiments, sample spaces, events, and probability laws, while investigating how they relate to communications engineering work. He goes on to discuss total probability theorems, after which he covers discrete random variables and continuous random variables. The author uses his years of teaching probability and random variable concepts to engineering students to form the text in a very understandable manner. The book features exercises, examples, case studies, and other key classroom materials
R 2,489.99
Probability and Statistical Inference
This book is in two volumes, and is intended as a text for introductory courses in probability and statistics at the second or third year university level. It em phasizes applications and logical principles rather than mathematical theory. A good background in freshman calculus is sufficient for most of the material presented. Several starred sections have been included as supplementary material. Nearly 900 problems and exercises of varying difficulty are given, and Appendix A contains answers to about one-third of them. The first volume (Chapters 1-8) deals with probability models and with math ematical methods for describing and manipulating them. It is similar in content and organization to the 1979 edition. Some sections have been rewritten and expanded-for example, the discussions of independent random variables and conditional probability. Many new exercises have been added. In the second volume (Chapters 9-16), probability models are used as the basis for the analysis and interpretation of data. This material has been revised extensively. Chapters 9 and 10 describe the use of the likelihood function in estimation problems, as in the 1979 edition. Chapter 11 then discusses frequency properties of estimation procedures, and introduces coverage probability and confidence intervals. Chapter 12 describes tests of significance, with applications primarily to frequency data. The likelihood ratio statistic is used to unify the material on testing, and connect it with earlier material on estimation.
R 2,781.45
Intelligent Autonomous Drones with Cognitive Deep Learning
What is an artificial intelligence (AI)-enabled drone and what can it do? Are AI-enabled drones better than human-controlled drones? This book will answer these questions and more, and empower you to develop your own AI-enabled drone.You'll progress from a list of specifications and requirements, in small and iterative steps, which will then lead to the development of Unified Modeling Language (UML) diagrams based in part to the standards established by for the Robotic Operating System (ROS). The ROS architecture has been used to develop land-based drones. This will serve as a reference model for the software architecture of unmanned systems. Using this approach you'll be able to develop a fully autonomous drone that incorporates object-oriented design and cognitive deep learning systems that adapts to multiple simulation environments. These multiple simulation environments will also allow you to further build public trust in the safety of artificial intelligence within drones and small UAS. Ultimately, you'll be able to build a complex system using the standards developed, and create other intelligent systems of similar complexity and capability.Intelligent Autonomous Drones with Cognitive Deep Learning uniquely addresses both deep learning and cognitive deep learning for developing near autonomous drones.What You’ll LearnExamine the necessary specifications and requirements for AI enabled drones for near-real time and near fully autonomous dronesLook at software and hardware requirementsUnderstand unified modeling language (UML) and real-time UML for designStudy deep learning neural networks for pattern recognitionReview geo-spatial Information for the development of detailed mission planning within these hostile environmentsWho This Book Is ForPrimarily for engineers, computer science graduate students, or even a skilled hobbyist. The target readers have the willingness to learn and extend the topic of intelligent autonomous drones. They should have a willingness to explore exciting engineering projects that are limited only by their imagination. As far as the technical requirements are concerned, they must have an intermediate understanding of object-oriented programming and design.
R 1,727.95
Analytical and Computational Methods in Probability Theory
This book constitutes the refereed proceedings of the First International Conference on Analytical and Computational Methods in Probability Theory and its Applications, ACMPT 2017, held in Moscow, Russia, in October 2017. The 42 full papers presented were carefully reviewed and selected from 173 submissions. The conference program consisted of four main themes associated with significant contributions made by A.D.Soloviev. These are:Analytical methods in probability theory,Computational methods in probability theory,Asymptotical methods in probability theory, the history of mathematics.
R 1,598.25
Empirical Approach to Machine Learning
This book provides a ‘one-stop source’ for all readers who are interested in a new, empirical approach to machine learning that, unlike traditional methods, successfully addresses the demands of today’s data-driven world. After an introduction to the fundamentals, the book discusses in depth anomaly detection, data partitioning and clustering, as well as classification and predictors. It describes classifiers of zero and first order, and the new, highly efficient and transparent deep rule-based classifiers, particularly highlighting their applications to image processing. Local optimality and stability conditions for the methods presented are formally derived and stated, while the software is also provided as supplemental, open-source material. The book will greatly benefit postgraduate students, researchers and practitioners dealing with advanced data processing, applied mathematicians, software developers of agent-oriented systems, and developers of embedded and real-time systems. It can also be used as a textbook for postgraduate coursework; for this purpose, a standalone set of lecture notes and corresponding lab session notes are available on the same website as the code.Dimitar Filev, Henry Ford Technical Fellow, Ford Motor Company, USA, and Member of the National Academy of Engineering, USA: “The book Empirical Approach to Machine Learning opens new horizons to automated and efficient data processing.”Paul J. Werbos, Inventor of the back-propagation method, USA: “I owe great thanks to Professor Plamen Angelov for making this important material available to the community just as I see great practical needs for it, in the new area of making real sense of high-speed data from the brain.” Chin-Teng Lin, Distinguished Professor at University of Technology Sydney, Australia: “This new book will set up a milestone for the modern intelligent systems.”Edward Tunstel, President of IEEE Systems, Man, Cybernetics Society, USA: “Empirical Approach to Machine Learning provides an insightful and visionary boost of progress in the evolution of computational learning capabilities yielding interpretable and transparent implementations.”
R 7,326.00
Applied Probability and Queues
"This book is a highly recommendable survey of mathematical tools and results in applied probability with special emphasis on queueing theory....The second edition at hand is a thoroughly updated and considerably expended version of the first edition.... This book and the way the various topics are balanced are a welcome addition to the literature. It is an indispensable source of information for both advanced graduate students and researchers." --MATHEMATICAL REVIEWS
R 3,871.07
Towards a Spin-Ensemble Quantum Memory for Superconducting Qubits
This work describes theoretical and experimental advances towards the realization of a hybrid quantum processor in which the collective degrees of freedom of an ensemble of spins in a crystal are used as a multi-qubit register for superconducting qubits. A memory protocol made of write, read and reset operations is first presented, followed by the demonstration of building blocks of its implementation with NV center spins in diamond. Qubit states are written by resonant absorption of a microwave photon in the spin ensemble and read out of the memory on-demand by applying Hahn echo refocusing techniques to the spins. The reset step is implemented in between two successive write-read sequences using optical repumping of the spins.
R 3,836.80
