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Authors: Stuart J. Russell (2021) - "Updated edition of popular textbook on Artificial Intelligence. This edition specific looks at ways of keeping artificial intelligence under control"--
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Authors: Colin de la Higuera (2024) - This open educational resource is designed for use by teachers to address the needs of teachers. This is the first version and it is anticipated that the book will evolve and it will explore how AI is being used to do schoolwork, how it could be best used (or not) in education and what to watch out for. The book is designed from the premise that it is important for all teachers to understand AI and to make informed choices when it comes to adopting or not adopting AI into their practice
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Authors: Jugnesh Kumar (2024) - Big data and analytics is an indispensable guide that navigates the complex data management and analysis. This comprehensive book covers the core principles, processes, and tools, ensuring readers grasp the essentials and progress to advanced applications
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Authors: Dmitry Vostokov (2024) - 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, you'll learn to use various debugging patterns through Python case studies that model abnormal software behavior. You'll also be exposed to Python debugging techniques...
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Authors: Ronald T. Kneusel (2024) - An accessible, straightforward guide that demystifies Artificial Intelligence for a general audience without the use of complex math or technical jargon. Covers the fundamentals, from classical models and neural networks to the large language models leading today's AI revolution
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Authors: Hardeo Kumar Thakur (2023) - The new book presents a valuable selection of new and state-of-the-art technological advancements in various application areas using the concepts of AI and machine learning, highlighting the use of predictive analytics of data from various application domains to find timely solutions to various problems. The book focuses on the research developments, limitations, and management of real-time problems using computational intelligence by identifying applicable approaches in order to enhance, automate, and develop effective solutions. The volume introduces empirical research, prospects of theoretical research, and applications in data science and artificial intelligence. The various novel...
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Authors: Chiranjibe Jana (2024) - Picture Fuzzy Logic and Its Applications in Decision Making Problems provides methodological frameworks and the latest empirical research findings in the field of picture fuzzy operators and their applications in scientific research and real-world engineering problems. In this book, picture fuzzy sets are investigated, and different types of operators are defined to solve a number of important decision-making and optimization problems.
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Authors: S. Karthikeyan (2024) - "This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, towards improving traditional machine learning algorithms through case-studies. Covers the core and fundamental aspects of statistics, quantum learning and quantum machines Discusses the basics of machine learning, regression, supervised and un-supervised machine learning algorithms, and artificial neural networks Elaborates upon quantum machine learning models, quantum machine learning approaches...
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Authors: Hemanth Kumar K (2023) - Google Cloud Vertex AI is a platform for machine learning (ML) offered by Google Cloud, with the objective of making the creation, deployment, and administration of ML models on a large scale easier. If you are seeking a unified and collaborative environment for your ML projects, this book is a valuable resource for you. This comprehensive guide is designed to help data enthusiasts effectively utilize Google Cloud Platform's Vertex AI for a wide range of machine learning operations. It covers the basics of the Google Cloud Platform, encompassing cloud storage, big query, and IAM. Subsequently, it delves into the specifics of Vertex AI, including AutoML, custom model training, model de...
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Authors: Mirza Rahim Baig (2024) - This book approaches data science solution building using a principled framework and case studies with extensive hands-on guidance. It will teach the readers optimization at each step, whether it is problem formulation or hyperparameter tuning for deep learning models. This book keeps the reader pragmatic and guides them toward practical solutions by discussing the essential ML concepts, including problem formulation, data preparation, and evaluation techniques. Further, the reader will be able to learn how to apply model optimization with advanced algorithms, hyperparameter tuning, and strategies against overfitting.
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Authors: Oswald Campesato (2024) - "This book is designed to provide the reader with basic Python 3 programming and ChatGPT concepts related to machine learning. The first chapter provides a fast-paced introduction to Python, Pandas and JSON. The next two chapters introduce the fundamental concepts of machine learning. The fourth chapter transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including ChatGPT, GPT-4, and their competitors, are presented to give readers an understanding of the current AI landscape. Chapters five through eight cover uses of GPT-4 in machine learning including linear regression, classifiers, clustering and data visualiza...
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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: 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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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: 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: Karm Veer Arya (2024) - "Artificial Intelligence and Machine Learning Techniques in Image Processing and Computer Vision provides in-depth and detailed knowledge about the latest research in image processing and computer vision techniques. It is a roadmap for the improvement of computer vision and image processing, explaining the machine learning algorithms and models involved. The authors differentiate between the various algorithms available and how to choose which to use for the most precise results for a specific task involving certain constraints. The volume provides real-world examples to illustrate the concepts and methods. The authors discuss machine learning in healthcare systems for detection, diag...
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Authors: Thomas N. Duening (2021) - Technology Entrepreneurship' provides a practical toolkit for potential entrepreneurs with technology backgrounds that will help them navigate complex issues such as raising capital, IP protection, product development, and more. The book's structure follows the entrepreneurial process in a step-by-step way, defining key terms and helping readers without business qualifications engage with the activities addressed. In addition, it covers a discussion of current trends and developments relevant for tomorrow's entrepreneurs. In-depth information on the practicalities of technology entrepreneurship are combined with experience from academics to provide a unique resource on how to approach...
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Authors: DR.C.THANAVATHI (2017) - This book is a synthesis of extensive collection of curriculum. This book is thus intended to make a contribution not only to the field of curriculum but also covers all the categories of curriculum development. This book seeks to describe and examine the processes of curriculum development and teachers-in-training with fundamental issues and practices in curriculum development. The book tries to provide as many examples as possible of how some of the practical problems in curriculum development have been addressed by practitioners in many parts of the world.
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Authors: Janna Hastings (2023) - AI for Scientific Discovery provides an accessible introduction to the wide-ranging applications of artificial intelligence technologies in scientific research and discovery across the full breadth of scientific disciplines. Artificial intelligence technologies support discovery science in multiple different ways. They support literature management and synthesis, allowing the wealth of what has already been discovered and reported on to be integrated and easily accessed. They play a central role in data analysis and interpretation - in the context of what is called 'data science'. AI is also helping to combat the reproducibility crisis in scientific research, by underpinning the disco...
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Authors: G. R. Kanagachidambaresan (2023) - This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis.
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