Machine Learning Foundations Methodologies And Applications

DOWNLOAD

Download Machine Learning Foundations Methodologies And Applications PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning Foundations Methodologies And Applications book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Machine Learning Foundations Methodologies And Applications

DOWNLOAD
Author :
language : en
Publisher:
Release Date :

Machine Learning Foundations Methodologies And Applications written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Handbook Of Research On Machine Learning

DOWNLOAD
Author : Monika Mangla
language : en
Publisher: CRC Press
Release Date : 2022-08-04

Handbook Of Research On Machine Learning written by Monika Mangla and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-04 with Computers categories.

This volume takes the reader on a technological voyage of machine learning advancements, highlighting the systematic changes in algorithms, challenges, and constraints. The technological advancements in the ML arena have transformed and revolutionized several fields, including transportation, agriculture, finance, weather monitoring, and others. This book brings together researchers, authors, industrialists, and academicians to cover a vast selection of topics in ML, starting with the rudiments of machine learning approaches and going on to specific applications in healthcare and industrial automation. The book begins with an overview of the ethics, security and privacy issues, future directions, and challenges in machine learning as well as a systematic review of deep learning techniques and provides an understanding of building generative adversarial networks. Chapters explore predictive data analytics for health issues. The book also adds a macro dimension by highlighting the industrial applications of machine learning, such as in the steel industry, for urban information retrieval, in garbage detection, in measuring air pollution, for stock market predictions, for underwater fish detection, as a fake news predictor, and more.

Explainable Ai Foundations Methodologies And Applications

DOWNLOAD
Author : Mayuri Mehta
language : en
Publisher: Springer Nature
Release Date : 2022-10-19

Explainable Ai Foundations Methodologies And Applications written by Mayuri Mehta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-19 with Technology & Engineering categories.

This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas. The book is suitable for students and academics aiming to build up their background on explainable AI and can guide them in making machine/deep learning models more transparent. The book can be used as a reference book for teaching a graduate course on artificial intelligence, applied machine learning, or neural networks. Researchers working in the area of AI can use this book to discover the recent developments in XAI. Besides its use in academia, this book could be used by practitioners in AI industries, healthcare industries, medicine, autonomous vehicles, and security surveillance, who would like to develop AI techniques and applications with explanations.

Derivative Free Optimization

DOWNLOAD
Author : Yang Yu
language : en
Publisher: Springer
Release Date : 2025-06-08

Derivative Free Optimization written by Yang Yu and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-08 with Mathematics categories.

This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book’s structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.

Machine Learning

DOWNLOAD
Author : Alexander Jung
language : en
Publisher: Springer Nature
Release Date : 2022-01-21

Machine Learning written by Alexander Jung and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-21 with Computers categories.

Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. This book approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods. The book’s three-component approach to ML provides uniform coverage of a wide range of concepts and techniques. As a case in point, techniques for regularization, privacy-preservation as well as explainability amount to specific design choices for the model, data, and loss of a ML method.

Robust Machine Learning

DOWNLOAD
Author : Rachid Guerraoui
language : en
Publisher: Springer
Release Date : 2024-05-03

Robust Machine Learning written by Rachid Guerraoui and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-03 with Computers categories.

Today, machine learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed machine learning scheme to be robust to these threats, and how to build provably robust machine learning algorithms. Studying the robustness of machine learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed machine learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe machine learning schemes. In addition to introducing the problem of robustness in modern machine learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. Moreover, the book provides a foundation for future research in this area.

Genetic And Evolutionary Computation Conference

DOWNLOAD
Author :
language : en
Publisher:
Release Date : 2005

Genetic And Evolutionary Computation Conference written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Genetic algorithms categories.

Gecco 2005

DOWNLOAD
Author : Hans-Georg Beyer
language : en
Publisher:
Release Date : 2005

Gecco 2005 written by Hans-Georg Beyer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Genetic algorithms categories.

Introduction To Transfer Learning

DOWNLOAD
Author : Jindong Wang
language : en
Publisher: Springer Nature
Release Date : 2023-03-30

Introduction To Transfer Learning written by Jindong Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-30 with Computers categories.

Transfer learning is one of the most important technologies in the era of artificial intelligence and deep learning. It seeks to leverage existing knowledge by transferring it to another, new domain. Over the years, a number of relevant topics have attracted the interest of the research and application community: transfer learning, pre-training and fine-tuning, domain adaptation, domain generalization, and meta-learning. This book offers a comprehensive tutorial on an overview of transfer learning, introducing new researchers in this area to both classic and more recent algorithms. Most importantly, it takes a “student’s” perspective to introduce all the concepts, theories, algorithms, and applications, allowing readers to quickly and easily enter this area. Accompanying the book, detailed code implementations are provided to better illustrate the core ideas of several important algorithms, presenting good examples for practice.

Introduction To Optimization Methods And Tools For Multidisciplinary Design In Aeronautics And Turbomachinery

DOWNLOAD
Author : Jacques Periaux
language : en
Publisher:
Release Date : 2008

Introduction To Optimization Methods And Tools For Multidisciplinary Design In Aeronautics And Turbomachinery written by Jacques Periaux and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Aerodynamics categories.