Machine learning

The book presents a collection of peer-reviewed articles from the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning-ICAAAIML 2021. The book covers research in the areas of artificial intelligence, machine learning, and deep learning applications in health care, agriculture, business, and security. This book contains research papers from academicians, researchers as well as students. There are also papers on core concepts of computer networks, intelligent system design and deployment, real-time systems, wireless sensor networks, sensors and sensor nodes, software engineering, and image processing. This book is a valuable resource for students, academics, and practitioners in the industry working on AI applications.

With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.

Transform your machine learning projects into successful deployments with this practical guide on how to build and scale solutions that solve real-world problemsIncludes a new chapter on generative AI and large language models (LLMs) and building a pipeline that leverages LLMs using LangChainKey FeaturesThis second edition delves deeper into key machine learning topics, CI/CD, and system designExplore core MLOps practices, such as model management and performance monitoringBuild end-to-end examples of deployable ML microservices and pipelines using AWS and open-source toolsBook DescriptionThe Second Edition of Machine Learning Engineering with Python is the practical guide that MLOps and ML engineers need to build solutions to real-world problems. It will provide you with the skills you need to stay ahead in this rapidly evolving field.The book takes an examples-based approach to help you develop your skills and covers the technical concepts, implementation patterns, and development methodologies you need. You'll explore the key steps of the ML development lifecycle and create your own standardized "model factory" for training and retraining of models. You'll learn to employ concepts like CI/CD and how to detect different types of drift.Get hands-on with the latest in deployment architectures and discover methods for scaling up your solutions. This edition goes deeper in all aspects of ML engineering and MLOps, with emphasis on the latest open-source and cloud-based technologies. This includes a completely revamped approach to advanced pipelining and orchestration techniques.With a new chapter on deep learning, generative AI, and LLMOps, you will learn to use tools like LangChain, PyTorch, and Hugging Face to leverage LLMs for supercharged analysis. You will explore AI assistants like GitHub Copilot to become more productive, then dive deep into the engineering considerations of working with deep learning.What you will learnPlan and manage end-to-end ML development projectsExplore deep learning, LLMs, and LLMOps to leverage generative AIUse Python to package your ML tools and scale up your solutionsGet to grips with Apache Spark, Kubernetes, and RayBuild and run ML pipelines with Apache Airflow, ZenML, and KubeflowDetect drift and build retraining mechanisms into your solutionsImprove error handling with control flows and vulnerability scanningHost and build ML microservices and batch processes running on AWSWho this book is forThis book is designed for MLOps and ML engineers, data scientists, and software developers who want to build robust solutions that use machine learning to solve real-world problems. If you're not a developer but want to manage or understand the product lifecycle of these systems, you'll also find this book useful. It assumes a basic knowledge of machine learning concepts and intermediate programming experience in Python. With its focus on practical skills and real-world examples, this book is an essential resource for anyone looking to advance their machine learning engineering career.Table of ContentsIntroduction to ML EngineeringThe Machine Learning Development ProcessFrom Model to Model Factory Packaging UpDeployment Patterns and ToolsScaling UpDeep Learning, Generative AI, and LLMOps Building an Example ML MicroserviceBuilding an Extract, Transform, Machine Learning Use Case

User care at home is a matter of great concern since unforeseen circumstances might occur that affect people's well-being. Technologies that assist people in independent living are essential for enhancing care in a cost-effective and reliable manner. Assisted care applications often demand real-time observation of the environment and the resident's activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the user care system in the literature to identify current practices for future research directions. Therefore, this book is aimed at a comprehensive review of data sources (e.g., sensors) with machine learning for various smart user care systems. To encourage the readers in the field, insights of practical essence of different machine learning algorithms with sensor data (e.g., publicly available datasets) are also discussed. Some code segments are also included to motivate the researchers of the related fields to practically implement the features and machine learning techniques. It is an effort to obtain knowledge of different types of sensor-based user monitoring technologies in-home environments. With the aim of adopting these technologies, research works, and their outcomes are reported. Besides, up to date references are included for the user monitoring technologies with the aim of facilitating independent living.Research that is related to the use of user monitoring technologies in assisted living is very widespread, but it is still consists mostly of limited-scale studies. Hence, user monitoring technology is a very promising field, especially for long-term care. However, monitoring of the users for smart assisted technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of people. The target of this book is to contribute towards that direction.

This book constitutes revised selected papers from the 18th International Conference on Web Information Systems and Technologies, WEBIST 2022, which took place in Valletta, Malta, in October 2022. The 13 full revised papers presented in this book were carefully reviewed and selected from a total of 62 submissions. The selected papers contribute to the understanding of relevant current research trends in Web information systems and technologies, including deep learning, knowledge representation and reasoning, recommender systems, internet of things, Web intelligence and big data.

If yoüre a complete newbie whös wondering exactly what ChatGPT is, what is does, and how it can be a valuable resource for non-coders, this is the book for you. With a comprehensive exploration of ChatGPT¿s features, foundations, and applications, this guide will serve as a valuable resource for beginners venturing into the world of conversational AI.ChatGPT for Beginners has four parts. Part one provides a step-by-step introduction to using ChatGPT, from accessing the OpenAI website and creating an account to starting conversations, changing responses, and getting conversation summaries. Part two delves into the foundations of the large language model that powers ChatGPT. It covers topics such as the definition of ChatGPT, its knowledge domains, the basics of natural language processing, machine learning techniques applied to language processing, and the role of neural networks in ChatGPT¿s operation. Part three covers a wide range of practical applications,from letter writing to business content creation, text translation, language learning, recruitment processes, artistic content creation, and fostering innovation and creativity. Part four examines the strengths and limitations of ChatGPT, addressing ethical considerations related to data security, bias, and partiality. It also delves into the future advancements and challenges that lie ahead for ChatGPT, providing insights into the developing landscape of conversational AI. After completing this book, you will be able to harness the full potential of ChatGPT. Whether you are a student, professional, or are just curious about the capabilities of this AI technology, this book will serve as your essential companion in unlocking the possibilities of ChatGPT. What You Will Learn Access ChatGPT, create an account, and initiate conversations Understand what ChatGPT is, its knowledge domains, and how it works through natural language processing, machine learning, and neural networks Ask relevant questions to ChatGPT, and obtain quality responses Manage the degree of randomness in the responses using parameters such as temperature, frequency, and presence Who This Book Is ForStudents, professionals, and anyone else curious about the capabilities of ChatGPT.

This book explores, from a high level, the parallels between the evolution of humans and the evolution of machines. The book reviews practical questions about the future of AI but also engages in philosophical discussions about what machine intelligence could mean for the human experience.The book focuses on what is intelligence and what separates intelligent species from non-so-intelligent ones. It concludes this section with the description of true nature of humanintelligence can be. We discuss how we looked at machines few hundred years back and how their definition and the expectations from them has changed over time. We will consider when and how machines became intelligent and then explore in depth he latest developments in artificial intelligence with explanation of deep learning technology and humanlike chat interface provided with products like ChatGPT. We will define both human intelligence and artificial intelligence and the distinction between the two.In the third and final section of the book, we will focus on near- and longer-term futures with widespread use of machine intelligence, making the whole ambient environment that we will live in intelligent How is this going to change human lives, and what parts of human life will be encroached with machines and their intelligence? We will explore how the job market will look with some jobs being taken by machines, and if this is overall a positive or negative change. What You Will LearnHow human intelligence is connected with artificial intelligence as well as the differencesHow AI is going to change our lives in the coming years, decades and centuriesAn explanation of deep learning technology and humanlike chat interface provided with products like ChatGPTWho This Book is ForReaders looking to contextualize the evolution of artificial intelligence in human history

Deep Learning Applications in Translational Bioinformatics, a new volume in the Advances in Ubiquitous Sensing Application for Healthcare series, offers a detailed overview of basic bioinformatics, deep learning, and various applications of deep learning in translational bioinformatics, including deep learning ensembles, deep learning in protein classification, detection of various diseases, prediction of antiviral peptides, identification of antibiotic resistance, computer aided drug design and drug formulation. This new volume helps researchers working in the field of machine learning and bioinformatics foster future research and development.

"Chun investigates the centrality of race, gender, class, and sexuality to "Big Data" and network analytics"--

This book covers different aspects of optimization autonomous underwater vehicles and their propulsion systems via machine learning techniques. It further analyses hydrodynamic characteristics including study of experimental investigation combined with hydrodynamic characteristics backed my MATLAB codes and simulation study results.

This book describes the application of machine learning modelling approaches in atomic layer deposition and presents detailed information on modelling, optimization, and prediction of the behaviour and characteristics of ALD for improved process quality control.

The text introduces students to numerous methods in solving a variety of Optimization problems. Also, the narrow focus of most math textbooks is completely dedicated to nonlinear programming, linear programming, combinatorial or convex optimization.

Generative Adversarial Networks (GANs) in Practice is an all-inclusive resource that provides a solid foundation on GAN methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts.

This book provides a comprehensive overview of machine learning algorithms and examines their application in complex decision-making systems in a service-oriented framework.

This book provides a comprehensive overview of machine learning algorithms and examines their application in complex decision-making systems in a service-oriented framework.

This book provides a comprehensive overview of methods to build comparable corpora and of their applications, including machine translation, cross-lingual transfer, and various kinds of multilingual natural language processing. The authors begin with a brief history on the topic followed by a comparison to parallel resources and an explanation of why comparable corpora have become more widely used. In particular, they provide the basis for the multilingual capabilities of pre-trained models, such as BERT or GPT. The book then focuses on building comparable corpora, aligning their sentences to create a database of suitable translations, and using these sentence translations to produce dictionaries and term banks. Then, it is explained how comparable corpora can be used to build machine translation engines and to develop a wide variety of multilingual applications.

Dieses Buch behandelt die gängigsten Methoden zur Klassifikation von digitalisierten Objekten. Jedem Objekt ist ein Punkt im Euklidischen Raum passender Dimension zugeordnet. Das Lernen basiert auf einer Menge von Punkten, für die die zugehörige Klasse bekannt ist. Eine Reduktion der Dimension sowie elementare und anspruchsvollere Methoden zur Ermittlung schnell berechenbarer Funktionen, mit denen man aus einem Punkt die zugehörige Klasse mit einer möglichst geringen Fehlerrate ableiten kann, werden hergeleitet und in einer einheitlichen Herangehensweise begründet. Die recht elementaren Beweise werden im Wesentlichen mit Mitteln der Linearen Algebra geführt, nur für die neuronalen Netze wird etwas Analysis benötigt.Die Produktfamilie WissensExpress bietet Ihnen Lehr- und Lernbücher in kompakter Form. Die Bücher liefern schnell und verständlich fundiertes Wissen.

Embark on an exhilarating journey into the realm of modern technological marvels with this comprehensive guide. Unveil the power of algorithms that can discern patterns in vast troves of data, opening doors to innovation and insight. Whether you're a tech enthusiast, a curious mind, or a seasoned programmer, "A Course in Machine Learning" invites you to demystify the enigmatic world of AI and data science.Within these pages, you'll unravel the intricacies of machine learning, guided by a seasoned expert who brings theory to life with real-world examples. Explore the algorithms that lie at the heart of self-driving cars, virtual assistants, and predictive analytics. Through hands-on exercises, sharpen your skills in creating intelligent systems that adapt and learn from experience.Dive into the realm of neural networks and deep learning, where layers of interconnected neurons mimic the human brain's astonishing capabilities. Grasp the art of feature engineering and data preprocessing to distill meaningful insights from noisy data. With step-by-step tutorials, you'll seamlessly transition from theory to practice, developing models that can decipher handwritten text, identify objects in images, and even predict future trends.Unlock the potential of unsupervised learning and reinforcement learning, letting algorithms uncover hidden patterns and optimize decision-making processes. From healthcare to finance, from entertainment to agriculture, the applications of machine learning are limitless. Gain the confidence to tackle real-world challenges and harness the power of data to transform industries and shape the future.Join the ranks of innovators who are reshaping our world through machine learning's unprecedented possibilities. Whether you're a student, a professional, or simply an inquisitive mind, "A Course in Machine Learning" equips you with the tools to unravel the complexities of AI and build a future that's driven by intelligence and imagination. Experience the thrill of discovery as you journey through these pages, guided by the wisdom of a true trailblazer in the field.

This book reviews the development of physics-based modeling and sensor-based data fusion for optimizing medical decision making in connection with spatiotemporal cardiovascular disease processes. To improve cardiac care services and patients¿ quality of life, it is very important to detect heart diseases early and optimize medical decision making. This book introduces recent research advances in machine learning, physics-based modeling, and simulation optimization to fully exploit medical data and promote the data-driven and simulation-guided diagnosis and treatment of heart disease. Specifically, it focuses on three major topics: computer modeling of cardiovascular systems, physiological signal processing for disease diagnostics and prognostics, and simulation optimization in medical decision making. It provides a comprehensive overview of recent advances in personalized cardiac modeling by integrating physics-based knowledge of the cardiovascular system with machine learning and multi-source medical data. It also discusses the state-of-the-art in electrocardiogram (ECG) signal processing for the identification of disease-altered cardiac dynamics. Lastly, it introduces readers to the early steps of optimal decision making based on the integration of sensor-based learning and simulation optimization in the context of cardiac surgeries. This book will be of interest to researchers and scholars in the fields of biomedical engineering, systems engineering and operations research, as well as professionals working in the medical sciences.

This book is a major update to the very successful first and second editions (2005 and 2010) of Data Mining and Knowledge Discovery Handbook. Since the last edition, this field has continued to evolve and to gain popularity. Existing methods are constantly being improved and new methods, applications and aspects are introduced. The new title of this handbook and its content reflect these changes thoroughly. Some existing chapters have been brought up to date. In addition to major revision of the existing chapters, the new edition includes totally new topics, such as: deep learning, explainable AI, human factors and social issues and advanced methods for big-data. The significant enhancement to the content reflects the growth in importance of data science. The third edition is also a timely opportunity to incorporate many other changes based on peers and students¿ feedback.This comprehensive handbook also presents a coherent and unified repository of data science major concepts, theories, methods, trends, challenges and applications. It covers all the crucial important machine learning methods used in data science.Today's accessibility and abundance of data make data science matters of considerable importance and necessity. Given the field's recent growth, it's not surprising that researchers and practitioners now have a wide range of methods and tools at their disposal. While statistics is fundamental for data science, methods originated from artificial intelligence, particularly machine learning, are also playing a significant role.This handbook aims to serve as the main reference for researchers in the fields of information technology, e-Commerce, information retrieval, data science, machine learning, data mining, databases and statistics as well as advanced level students studying computer science or electrical engineering. Practitioners working within these related fields and data scientists will also want to purchase this handbook as a reference.

This volume comprises the select peer-reviewed proceedings of the International Conference on Advances and Applications of Artificial Intelligence and Machine Learning 2022 (ICAAAIML 2022). It aims to provide a comprehensive and broad-spectrum picture of state-of-the-art research and development in the areas of artificial intelligence, machine learning, deep learning, and their advanced applications in computer vision and blockchain. It also covers research in core concepts of computers, intelligent system design and deployment, real-time systems, WSN, sensors and sensor nodes, software engineering, image processing, and cloud computing. This volume will provide a valuable resource for those in academia and industry.

Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools.Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Naïve Bayes, and neural networks.After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary toapply these methods using PySpark, the industry standard for building scalable ML data pipelines.What You Will LearnMaster the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systemsUnderstand the differences between PySpark, scikit-learn, and pandasPerform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySparkDistinguish between the pipelines of PySpark and scikit-learn Who This Book Is ForData scientists, data engineers, and machine learning practitioners who have some familiarity with Python, but who are new to distributed machine learning and the PySpark framework.

This book is for those who wish to understand how Python debugging is and can be used to develop robust and reliable AI, machine learning, and cloud computing software. It will teach you a novel pattern-oriented approach to diagnose and debug abnormal software structure and behavior.The book begins with an introduction to the pattern-oriented software diagnostics and debugging process that, before performing Python debugging, diagnoses problems in various software artifacts such as memory dumps, traces, and logs. Next, yoüll learn to use various debugging patterns through Python case studies that model abnormal software behavior. Yoüll also be exposed to Python debugging techniques specific to cloud native and machine learning environments and explore how recent advances in AI/ML can help in Python debugging. Over the course of the book, case studies will show you how to resolve issues around environmental problems, crashes, hangs, resource spikes, leaks, and performancedegradation. This includes tracing, logging, and analyzing memory dumps using native WinDbg and GDB debuggers. Upon completing this book, you will have the knowledge and tools needed to employ Python debugging in the development of AI, machine learning, and cloud computing applications.What You Will LearnEmploy a pattern-oriented approach to Python debugging that starts with diagnostics of common software problemsUse tips and tricks to get the most out of popular IDEs, notebooks, and command-line Python debuggingUnderstand Python internals for interfacing with operating systems and external modulesPerform Python memory dump analysis, tracing, and loggingWho This Book Is ForSoftware developers, AI/ML engineers, researchers, data engineers, as well as MLOps and DevOps professionals.

If you are a decision maker, leader in your organization, government of¿cial, or setting your career goals to be the future a leader in your an organization, then this is a must read.This is the ¿rst book that links Decision Theory from psychology literature, with Game Theory from behavioral economy literature, together with Machine Learning from Computer Science literature in just one book for decision makers.This book is not about how to make decisions. The goal of this ¿eld guide, for decision makers, is to explore and explain the challenges that leaders face when making decisions. Greater level of understanding about the various in¿uences that come into the decision making process, can yield enhanced outcomes.The aim of this book is to empower leaders with a comprehensive guide to the array of both objective and subjective factors that are commonly used to evaluate choices. Once there is awareness and clarity about the potential in¿uences in each of the objective and subjective decisions, then there is the potential to make decisions optimally and most importantly, decisively.

The main goal of this comprehensive textbook is to cover the core techniques required to understand some of the basic and most popular model learning algorithms available for engineers, then illustrate their applicability directly with stationary time series. A multi-step approach is introduced for modeling time series which differs from the mainstream in the literature. Singular spectrum analysis of univariate time series, trend and seasonality modeling with least squares and residual analysis, and modeling with ARMA models are discussed in more detail. As applications of data-driven model learning become widespread in society, engineers need to understand its underlying principles, then the skills to develop and use the resulting data-driven model learning solutions. After reading this book, the users will have acquired the background, the knowledge and confidence to (i) read other model learning textbooks more easily, (ii) use linear algebra and statistics for data analysis and modeling, (iii) explore other fields of applications where model learning from data plays a central role. Thanks to numerous illustrations and simulations, this textbook will appeal to undergraduate and graduate students who need a first course in data-driven model learning. It will also be useful for practitioners, thanks to the introduction of easy-to-implement recipes dedicated to stationary time series model learning. Only a basic familiarity with advanced calculus, linear algebra and statistics is assumed, making the material accessible to students at the advanced undergraduate level.

This open access book gathers authors from a wide range of social-scientific and engineering disciplines to review challenges from their respective fields that arise from the processes of social and technological transformation taking place worldwide. The result is a much-needed collection of knowledge about the integration of social, organizational and technical challenges that need to be tackled to uphold safety in the digital age.The contributors whose work features in this book help their readers to navigate the massive increase in the capability to generate and use data in developing algorithms intended for automation of work, machine learning and next-generation artificial intelligence and the blockchain technology already in such extensive use in real-world organizations.This book deals with such issues as:· How can high-risk and safety-critical systems be affected by these developments, in terms of their activities, theirorganization, management and regulation?· What are the sociotechnical challenges of the proliferation of big data, algorithmic influence and cyber-security challenges in health care, transport, energy production/distribution and production of goods?Understanding the ways these systems operate in the rapidly changing digital context has become a core issue for academic researchers and other experts in safety science, security and critical-infrastructure protection. The research presented here offers a lens through which the reader can grasp the way such systems evolve and the implications for safety¿an increasingly multidisciplinary challenge that this book does not shrink from addressing.

This is the first technical book that considers tests as public tools and examines how to engineer and process test data, extract the structure within the data to be visualized, and thereby make test results useful for students, teachers, and the society. The author does not differentiate test data analysis from data engineering and information visualization. This monograph introduces the following methods of engineering or processing test data, including the latest machine learning techniques: classical test theory (CTT), item response theory (IRT), latent class analysis (LCA), latent rank analysis (LRA), biclustering (co-clustering), and Bayesian network model (BNM). CTT and IRT are methods for analyzing test data and evaluating students' abilities on a continuous scale. LCA and LRA assess examinees by classifying them into nominal and ordinal clusters, respectively, where the adequate number of clusters is estimated from the data. Biclustering classifies examinees into groups (latent clusters) while classifying items into fields (factors). Particularly, the infinite relational model discussed in this book is a biclustering method feasible under the condition that neither the number of groups nor the number of fields is known beforehand. Additionally, the local dependence LRA, local dependence biclustering, and bicluster network model are methods that search and visualize inter-item (or inter-field) network structure using the mechanism of BNM. As this book offers a new perspective on test data analysis methods, it is certain to widen readers' perspective on test data analysis.