Provides an overview of machine learning, both for a clinical and engineering audience Summarize recent advances in both cardiovascular medicine and artificial intelligence Discusses the advantages of using machine learning for outcomes research and image processing Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach Artificial Intelligence and Machine Learning in Healthcare It is suitable for both clinicians and engineers providing a template for clinicians to understand areas of application of machine learning within cardiovascular research and assist computer scientists and engineers in evaluating current and future impact of machine learning on cardiovascular medicine. It helps readers understand how ML works along with its limitations and strengths, such that they can could harness its computational power to streamline workflow and improve patient care. The book focuses on emphasizing ML for biomedical applications and provides a comprehensive summary of the past and present of AI, basics of ML, and clinical applications of ML within cardiovascular medicine for predictive analytics and precision medicine. Machine Learning in Cardiovascular Medicine addresses the ever-expanding applications of artificial intelligence (AI), specifically machine learning (ML), in healthcare and within cardiovascular medicine. Machine Learning in Cardiovascular Medicine
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FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval.
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It also covers numerous applications in healthcare, education, communication, media, and entertainment. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. Indian Geographyĭata science revolves around two giants: Big Data analytics and Deep Learning. It is a good resource for mathematics teachers, educators and research students. The book makes a significant contribution towards the learning of mathematics. Three broad themes, namely fundamentals, instructional tools, and approaches to teaching for nurturing reflective learners in mathematics classrooms, shape the ideas in these chapters.
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The fifteen chapters of the book illustrate evidence-based practices that school teachers and researchers can experiment with in their own classrooms to bring about meaningful learning outcomes. The objective is to encourage teachers and researchers to advance reflection among students and teachers in mathematics classrooms.Several renowned international and Singapore researchers in the field have published their work in this volume.
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This fifth volume in the series of yearbooks by the Association of Mathematics Educators in Singapore entitled Nurturing Reflective Learners in Mathematics is unique in that it focuses on a single theme in mathematics education.