Dasu Priyanka
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Handbook of Deep Learning Models for Healthcare Data Processing
In recent years, deep learning has shown great potential in transforming various fields including healthcare. With the abundance of healthcare data being generated every day, there is a pressing need to develop efficient algorithms that can process and analyze this data to improve patient care and treatment outcomes. Handbook of Deep Learning Models for Healthcare Data Processing: Disease Prediction, Analysis, and Applications covers a wide range of deep learning models, techniques, and applications in healthcare data processing, analysis, and disease prediction, providing a comprehensive overview of the field. It focuses on the practical application of deep learning models in healthcare and offers step-by-step instructions for building and deploying models and using real-world examples. The handbook discusses the potential future applications of deep learning models in healthcare, such as precision medicine, personalized treatment, and clinical decision support. It also addresses the ethical considerations associated with the use of deep learning models in healthcare, such as privacy, security, and bias. It provides technical details on deep learning models, including their architecture, training methods, and optimization techniques, making it useful for data scientists and researchers. Written to be a comprehensive guide for healthcare professionals, researchers, and data analysts, this handbook is an essential need for those who are interested in using deep learning models to analyze and process healthcare data. It is also suitable for those who have a basic understanding of machine learning and want to learn more about the latest advancements in deep learning in healthcare.
Applied Machine Learning: From Theory to Deployment
Author: Dr. Nagesh S. Salimath
language: en
Publisher: Xoffencer International Book Publication House
Release Date: 2025-03-05
Numerous sectors have been revolutionized machine learning (ML), which has made it possible to make decisions based on data and to automate processes. This book examines the whole machine learning pipeline, beginning with theoretical underpinnings and ending with implementation in the actual world. In this section, we discuss fundamental algorithms, methodologies for training models, assessment methods, and optimization strategies. There includes a comprehensive discussion on practical elements such as the preparation of data, the engineering of features, and the monitoring of hyperparameters. In addition, we examine the problems that pertain to the deployment of machine learning models, which include scalability, interpretability, and ethical considerations. Readers will be equipped with the abilities necessary to construct, assess, and deploy solid machine learning solutions in a variety of domains by reading this book, which bridges the gap between theory and actual application.
The Seaside Bride and Other Stories
Dasu Krishanmoorty, in his ninety fourth year, has immense faith in the power of memories to bring back days already lived for a re-evaluation, a leveling of highs and lows. Eventually, every man searches his past for clues, seeking to solve the enigma of being and the negotiation of a life not of his making. Dasu's personal tales are spun from four significant migrations that defined his life, tracing his lineage to an ancestor who was also a migrant. He takes the reader to places with different histories, geographies, and cultures. Three of these locations, once ruled by Muslim dynasties, are open-air museums of royal tombs and homes to awe-inspiring Islamic marvels of architecture: Hyderabad, Ahmedabad, and New Delhi. In contrast, the setting of the first story in Dasu's collection, ?Cracking Infinity, ? epitomizes modern minimalism in the United States, the location of his last migration.