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Advanced Computing, Machine Learning, Robotics and Internet Technologies - - Bog - Springer International Publishing AG - Plusbog.dk

Advanced Computing, Machine Learning, Robotics and Internet Technologies - - Bog - Springer International Publishing AG - Plusbog.dk

This two-volume set constitutes selected papers presented during the First International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023, held in Silchar, India, in March 2023. The 20 full papers and 27 short papers presented were thoroughly reviewed and selected from 110 submissions. They cover the following topics: artificial intelligence, machine learning, natural language processing, image processing, data science, soft computing techniques, computer networks and security, computer architecture and algorithms.

DKK 551.00
1

Advanced Computing, Machine Learning, Robotics and Internet Technologies - - Bog - Springer International Publishing AG - Plusbog.dk

Advanced Computing, Machine Learning, Robotics and Internet Technologies - - Bog - Springer International Publishing AG - Plusbog.dk

This two-volume set constitutes selected papers presented during the First International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023, held in Silchar, India, in March 2023. The 20 full papers and 27 short papers presented were thoroughly reviewed and selected from 110 submissions. They cover the following topics: artificial intelligence, machine learning, natural language processing, image processing, data science, soft computing techniques, computer networks and security, computer architecture and algorithms.

DKK 551.00
1

Applied Machine Learning Using mlr3 in R - - Bog - Taylor & Francis Ltd - Plusbog.dk

Applied Machine Learning Using mlr3 in R - - Bog - Taylor & Francis Ltd - Plusbog.dk

mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components. Features: - In-depth coverage of the mlr3 ecosystem for users and developers - Explanation and illustration of basic and advanced machine learning concepts - Ready to use code samples that can be adapted by the user for their application - Convenient and expressive machine learning pipelining enabling advanced modelling - Coverage of topics that are often ignored in other machine learning books The book is primarily aimed at researchers, practitioners, and graduate students who use machine learning or who are interested in using it. It can be used as a textbook for an introductory or advanced machine learning class that uses R, as a reference for people who work with machine learning methods, and in industry for exploratory experiments in machine learning.

DKK 656.00
1

Weighing Lives in War - - Bog - Oxford University Press - Plusbog.dk

Weighing Lives in War - - Bog - Oxford University Press - Plusbog.dk

The chief means to limit and calculate the costs of war are the philosophical and legal concepts of proportionality and necessity. Both categories are meant to restrain the most horrific potential of war. The volume explores the moral and legal issues in the modern law of war in three major categories. In so doing, the contributions will look for new and innovative approaches to understanding the process of weighing lives implicit in all theories of jus in bello: who counts in war, understanding proportionality, and weighing lives in asymmetric conflicts. These questions arise on multiple levels and require interdisciplinary consideration of both philosophical and legal themes.

DKK 454.00
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Machine Learning for Tabular Data - Mark Ryan - Bog - Manning Publications - Plusbog.dk

Machine Learning for Tabular Data - Mark Ryan - Bog - Manning Publications - Plusbog.dk

Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques. Every organization in the world stores data in tables. Advanced Analytics for Business reveals practical techniques for applying machine learning techniques like deep learning and gradient boosting to your company''s rows and columns.Inside Advanced Analytics for Business you''ll learn how to: - - Pick the right machine learning approach for your data - - Apply deep learning to tabular data - - Deploy tabular machine learning locally and in the cloud - - Pipelines to automatically train and maintain a model - This book collects best practices, hard-won tips and tricks, and hands-on techniques for making sense of tabular data using advanced machine learning techniques. Inside, you''ll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline.

DKK 572.00
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Machine Learning - Jugal Kalita - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning - Jugal Kalita - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples. Features: - Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students, and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding, enabling further exploration Presents worked out suitable programming examples, thus ensuring conceptual, theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth, within limits of what can be taught in a short period of time. Thus, the book can provide foundations that will empower a student to read advanced books and research papers.

DKK 468.00
1

Coverbal Synchrony in Human-Machine Interaction - - Bog - Taylor & Francis Ltd - Plusbog.dk

Coverbal Synchrony in Human-Machine Interaction - - Bog - Taylor & Francis Ltd - Plusbog.dk

Embodied conversational agents (ECA) and speech-based human–machine interfaces can together represent more advanced and more natural human–machine interaction. Fusion of both topics is a challenging agenda in research and production spheres. The important goal of human–machine interfaces is to provide content or functionality in the form of a dialog resembling face-to-face conversations. All natural interfaces strive to exploit and use different communication strategies that provide additional meaning to the content, whether they are human–machine interfaces for controlling an application or different ECA-based human–machine interfaces directly simulating face-to-face conversation. Coverbal Synchrony in Human-Machine Interaction presents state-of-the-art concepts of advanced environment-independent multimodal human–machine interfaces that can be used in different contexts, ranging from simple multimodal web-browsers (for example, multimodal content reader) to more complex multimodal human–machine interfaces for ambient intelligent environments (such as supportive environments for elderly and agent-guided household environments). They can also be used in different computing environments—from pervasive computing to desktop environments. Within these concepts, the contributors discuss several communication strategies, used to provide different aspects of human–machine interaction.

DKK 643.00
1

Applications of Optimization and Machine Learning in Image Processing and IoT - - Bog - Taylor & Francis Ltd - Plusbog.dk

Applications of Optimization and Machine Learning in Image Processing and IoT - - Bog - Taylor & Francis Ltd - Plusbog.dk

This book presents state-of-the-art optimization algorithms followed by Internet of Things (IoT) fundamentals. The applications of machine learning and IoT are explored, with topics including optimization, algorithms and machine learning in image processing and IoT. Applications of Optimization and Machine Learning in Image Processing and IoT is a complete reference source, providing the latest research findings and solutions for optimization and machine learning algorithms. The chapters examine and discuss the fields of machine learning, IoT and image processing. KEY FEATURES: • Includes fundamental concepts towards advanced applications in machine learning and IoT. • Discusses potential and challenges of machine learning for IoT and optimization • Reviews recent advancements in diverse researches on computer vision, networking and optimization field. • Presents latest technologies such as machine learning in image processing and IoT This book has been written for readers in academia, engineering, IT specialists, researchers, industrial professionals and students, and is a great reference for those just starting out in the field as well as those at an advanced level.

DKK 542.00
1

Signal Processing and Machine Learning with Applications - Michael M. Richter - Bog - Springer International Publishing AG - Plusbog.dk

Signal Processing and Machine Learning with Applications - Michael M. Richter - Bog - Springer International Publishing AG - Plusbog.dk

Signal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents different kinds of signals humans use and applies them for human machine interaction to communicate. Signal Processing and Machine Learning with Applications presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications. It is organized in three parts: Realms of Signal Processing; Machine Learning and Recognition; and Advanced Applications and Artificial Intelligence. The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes. The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engaged with signal processing, machine learning and the applications.

DKK 426.00
1

Machine Learning - Zhi Hua Zhou - Bog - Springer Verlag, Singapore - Plusbog.dk

Machine Learning - Zhi Hua Zhou - Bog - Springer Verlag, Singapore - Plusbog.dk

Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.

DKK 519.00
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Advanced Manufacturing Methods - - Bog - Taylor & Francis Ltd - Plusbog.dk

Advanced Manufacturing Methods - - Bog - Taylor & Francis Ltd - Plusbog.dk

Advanced Manufacturing Methods: Smart Processes and Modeling for Optimization describes developments in advanced manufacturing processes and applications considering typical and advanced materials. It helps readers implement manufacturing 4.0 production techniques and highlights why a consolidated source and robust platform are necessary for implementing machine learning processes in the manufacturing sector. - Discusses the industrial impact of manufacturing process - Provides novel fundamental manufacturing solutions - Presents the various aspects of applications in advanced materials in correlation of physical properties with macro-, micro- and nanostructures - Reviews both classical and artificial manufacturing when applied with typical and novel innovative materials Aimed at those working in manufacturing, mechanical and optimization of manufacturing processes, this work provides readers with a comprehensive view of current development in, and applications of, advanced manufacturing.

DKK 542.00
1

Python Machine Learning By Example - Yuxi Liu - Bog - Packt Publishing Limited - Plusbog.dk

Python Machine Learning By Example - Yuxi Liu - Bog - Packt Publishing Limited - Plusbog.dk

Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandasKey FeaturesDiscover new and updated content on NLP transformers, PyTorch, and computer vision modelingIncludes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutionsImplement ML models, such as neural networks and linear and logistic regression, from scratchPurchase of the print or Kindle book includes a free PDF copyBook DescriptionThe fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.What you will learnFollow machine learning best practices throughout data preparation and model developmentBuild and improve image classifiers using convolutional neural networks (CNNs) and transfer learningDevelop and fine-tune neural networks using TensorFlow and PyTorchAnalyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIPBuild classifiers using support vector machines (SVMs) and boost performance with PCAAvoid overfitting using regularization, feature selection, and moreWho this book is forThis expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.

DKK 428.00
1

Advanced Computing - - Bog - Springer Verlag, Singapore - Plusbog.dk

Advanced Computing - - Bog - Springer Verlag, Singapore - Plusbog.dk

This two-volume set (CCIS 1367-1368) constitutes reviewed and selected papers from the 10th International Advanced Computing Conference, IACC 2020, held in December 2020. The 65 full papers and 2 short papers presented in two volumes were thorougly reviewed and selected from 286 submissions. The papers are organized in the following topical sections: Application of Artificial Intelligence and Machine Learning in Healthcare; Using Natural Language Processing for Solving Text and Language related Applications; Using Different Neural Network Architectures for Interesting applications; ​Using AI for Plant and Animal related Applications.- Applications of Blockchain and IoT.- Use of Data Science for Building Intelligence Applications; Innovations in Advanced Network Systems; Advanced Algorithms for Miscellaneous Domains; New Approaches in Software Engineering.

DKK 434.00
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Combating Women's Health Issues with Machine Learning - - Bog - Taylor & Francis Ltd - Plusbog.dk

Combating Women's Health Issues with Machine Learning - - Bog - Taylor & Francis Ltd - Plusbog.dk

The main focus of this book is the examination of women’s health issues and the role machine learning can play as a solution to these challenges. This book will illustrate advanced, innovative techniques/frameworks/concepts/machine learning methodologies, enhancing the future healthcare system. Combating Women’s Health Issues with Machine Learning: Challenges and Solutions examines the fundamental concepts and analysis of machine learning algorithms. The editors and authors of this book examine new approaches for different age-related medical issues that women face. Topics range from diagnosing diseases such as breast and ovarian cancer to using deep learning in prenatal ultrasound diagnosis. The authors also examine the best machine learning classifier for constructing the most accurate predictive model for women’s infertility risk. Among the topics discussed are gender differences in type 2 diabetes care and its management as it relates to gender using artificial intelligence. The book also discusses advanced techniques for evaluating and managing cardiovascular disease symptoms, which are more common in women but often overlooked or misdiagnosed by many healthcare providers. The book concludes by presenting future considerations and challenges in the field of women’s health using artificial intelligence. This book is intended for medical researchers, healthcare technicians, scientists, programmers and graduate-level students looking to understand better and develop applications of machine learning/deep learning in healthcare scenarios, especially concerning women’s health conditions.

DKK 514.00
1

Machine Learning on Geographical Data Using Python - Joos Korstanje - Bog - APress - Plusbog.dk

Machine Learning on Geographical Data Using Python - Joos Korstanje - Bog - APress - Plusbog.dk

Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. What You Will Learn - Understand the fundamental concepts of working with geodata - Work with multiple geographical data types and file formats in Python - Create maps in Python - Apply machine learning on geographical data Who This Book Is For Readers with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environment

DKK 468.00
1

Algorithms in Advanced Artificial Intelligence - - Bog - Taylor & Francis Ltd - Plusbog.dk

Algorithms in Advanced Artificial Intelligence - - Bog - Taylor & Francis Ltd - Plusbog.dk

Algorithms in Advanced Artificial Intelligence is a collection of papers on emerging issues, challenges, and new methods in Artificial Intelligence, Machine Learning, Deep Learning, Cloud Computing, Federated Learning, Internet of Things, and Blockchain technology. It addresses the growing attention to advanced technologies due to their ability to provide “paranormal solutions” to problems associated with classical Artificial Intelligence frameworks. AI is used in various subfields, including learning, perception, and financial decisions. It uses four strategies: Thinking Humanly, Thinking Rationally, Acting Humanly, and Acting Rationally. The authors address various issues in ICT, including Artificial Intelligence, Machine Learning, Deep Learning, Data Science, Big Data Analytics, Vision, Internet of Things, Security and Privacy aspects in AI, and Blockchain and Digital Twin Integrated Applications in AI.

DKK 594.00
1

Automated Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

Automated Machine Learning - - Bog - Springer Nature Switzerland AG - Plusbog.dk

This open access book presents the first comprehensive overview of general methods in Automatic Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first international challenge of AutoML systems. The book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. Many of the recent machine learning successes crucially rely on human experts, who select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters; however the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself.

DKK 434.00
1

Machine Learning - Andreas Lindholm - Bog - Cambridge University Press - Plusbog.dk

Machine Learning - Andreas Lindholm - Bog - Cambridge University Press - Plusbog.dk

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.

DKK 571.00
1

Prediction and Analysis for Knowledge Representation and Machine Learning - - Bog - Taylor & Francis Ltd - Plusbog.dk

Prediction and Analysis for Knowledge Representation and Machine Learning - - Bog - Taylor & Francis Ltd - Plusbog.dk

A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems. Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book’s website. Features: Examines the representational adequacy of needed knowledge representation Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology Describes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which include both basic and advanced concepts.

DKK 542.00
1

Real-World Machine Learning - Henrick Brink - Bog - Manning Publications - Plusbog.dk

Real-World Machine Learning - Henrick Brink - Bog - Manning Publications - Plusbog.dk

DESCRIPTION In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods. KEY FEATURES - - Accessible and practical introduction to machine learning - - Contains big-picture ideas and real-world examples - - Prepares reader to build and deploy powerful predictive systems - - Offers tips & tricks and highlights common pitfalls - AUDIENCE Code examples are in Python and R. No prior machine learning experience required. ABOUT THE TECHNOLOGY Machine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all demanding machine learning skills from their developers.

DKK 398.00
1