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Authors: Kevin P. Murphy (2012) - The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics.
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Authors: Ameet V Joshi (2020) - This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state.
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Authors: Huixiao Hong (2023) - This book is expected to provide a reference for practical applications of machine learning and deep learning in toxicological research. It is a useful guide for toxicologists, chemists, drug discovery and development researchers, regulatory scientists, government reviewers, and graduate students.
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Authors: Anitha S. Pillai (2024) - Natural language processing (Computer science) | Machine learning | Deep learning (Machine learning) | Khoa học máy tính
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Authors: Ben Othman Soufiene (2024) - "Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory, and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. The book offers important key aspects in the development and implementat...
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Authors: Leandros A. Maglaras (2024) - This book addresses the growing need for a comprehensive guide to the application of machine learning in financial analytics. It offers a valuable resource for both beginners and experienced professionals in finance and data science by covering the theoretical foundations, practical implementations, ethical considerations, and future trends in the field. It bridges the gap between theory and practice, providing readers with the tools and knowledge they need to leverage the power of machine learning in the financial sector responsibly
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Authors: Vũ Hữu Tiệp (2018) - Hướng dẫn các bạn trẻ làm quen các khái niệm, kỹ thuật và thuật toán cơ bản cho các bài toán Học máy (ML). Những khái niệm cơ bản trong ML, xây dựng các mô hình ML, các thuật toán ML phổ biến như mạng neuron nhân tạo, kỹ thuật tối ưu phổ biến cho các bài toán tối ưu không ràng buộc
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Authors: Eklas Hossain (2024) - The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly.
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Authors: Andriy Burkov (2020) - The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.
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Authors: Galit Shmueli (2023) - Machine learning--also known as data mining or data analytics-- is a fundamental part of data science. It is used by organizationsin a wide variety of arenas to turn raw data into actionableinformation. Machine Learning for Business Analytics: Concepts, Techniques, and Applications in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation and network analytics. Along with hands-on exercises and real-life case studies, it also discusses ...
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Authors: Esteban Tlelo-Cuautle (2023) - "This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The main topics covered under this title include: machine learning, artificial intelligence, cryptography, submarines, drones, security in healthcare, Internet of Things and robotics. This book can be used by graduate students, industrial and academic professionals to revise real case studies in applying machine learning in the areas of modeling, simulation and optimization of complex systems, cryptography, electronics, healthcare, control systems, Internet of Things, security, and unmanned systems such as submarines, drones and robots"
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Authors: Patanjali Kashyap (2024) - This new and updated edition takes you through the details of machine learning to give you an understanding of cognitive computing, IoT, big data, AI, quantum computing, and more. The book explains how machine learning techniques are used to solve fundamental and complex societal and industry problems. This second edition builds upon the foundation of the first book, revises all of the chapters, and updates the research, case studies, and practical examples to bring the book up to date with changes that have occurred in machine learning. A new chapter on quantum computers and machine learning is included to prepare you for future challenges.
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Authors: Isaiah Hull (2021) - This book is structured to teach through a sequence of complete examples, each framed in terms of a specific economic problem of interest or topic. Otherwise complicated content is then distilled into accessible examples, so you can use TensorFlow to solve workhorse models in economics and finance. You will: Define, train, and evaluate machine learning models in TensorFlow 2 Apply fundamental concepts in machine learning, such as deep learning and natural language processing, to economic and financial problems Solve workhorse models in economics and finance
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Authors: Hall, Patrick (2023) - This book describes approaches to responsible AI—a holistic framework for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science. Authors Patrick Hall, James Curtis, and Parul Pandey created this guide for data scientists who want to improve real-world AI/ML system outcomes for organizations, consumers, and the public.
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Authors: N. M. Anoop Krishnan (2024) - This book covers broad areas of data-driven modeling, ranging from simple regression to advanced machine learning and optimization methods for applications in materials modeling and discovery. The book explains complex mathematical concepts in a lucid manner to ensure that readers from different materials domains are able to use these techniques successfully. A unique feature of this book is its hands-on aspecteach method presented herein is accompanied by a code that implements the method in open-source platforms such as Python.
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Authors: Sinh Cong Lam (2024) - "The book "Machine Learning for Mobile Communications" will take readers on a journey from the basic to advanced knowledge about mobile communications and machine learning. For basic levels, this book volume discusses a wide range of mobile communications topics from the system level such as system design, optimization to the user level such as power control, resource allocation. It also reviews state-of-art Machine Learning which is one of the biggest emerging trends for both academic and industrials. For the advanced level, this book provides knowledge about how to utilize Machine Learning to design and solve the problems of future mobile communications. It discusses solutions for l...
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Authors: Tulsi Pawan Fowdur (2024) - This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using JavaScript. JavaScript provides the flexibility to deploy these applications across various devices and web browsers. The book begins by delving into NTMA, explaining fundamental concepts and providing an overview of existing applications and research within this domain. It also goes into the essentials of VQA and offers a survey of the latest developments in VQA algorithms.
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Authors: Michael Beyeler (2017) - This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own machine learning systems, tailored to practical real-world tasks.
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Authors: Ruiz, Ignacio (2022) - "The computational demand of risk calculations in financial institutions has ballooned. Traditionally, this has led to the acquisition of more and more computer power -- some banks have farms in the order of 50,000 CPUs, with running costs in the multimillions of dollars -- but this path is no longer economically or operationally viable. Algorithmic solutions represent a viable way to reduce costs while simultaneously increasing risk calculation capabilities
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Authors: Amita Nandal (2023) - This edited book explores new and emerging technologies in the field of medical image processing using deep learning models, neural networks and machine learning architectures. Multimodal medical imaging and optimisation techniques are discussed in relation to the advances, challenges and benefits of computer-aided diagnoses
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