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Authors: Mohd Hafiz Arzmi (2023) - In this brief, the diagnosis of four types of common cancers, i.e., breast, lung, oral and skin, are evaluated with different state-of-the-art feature-based transfer learning models. It is expected that the findings in this book are insightful to various stakeholders in the diagnosis of cancer
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Authors: Akshay R. Kulkarni (2023) - This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average).
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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: 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 this crucial subject
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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 long-term problems such as resource allocation, security, power control, and spectral efficiency. Thi...
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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 visualization including DALL-E"
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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: 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 and quantum classification, boosting. Introduces quantum evaluation models, deep quantum learning, ...
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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 deployment on endpoints, development of Vertex AI pipelines, and the Explainable AI feature store. By ...
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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, diagnosis, classification, and segmentation. They also explore the diverse applications of image and vid...
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