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Computational Imaging and Analytics in Biomedical Engineering: Algorithms and Applications

Автор: Limpopo5 от 2024-03-22, 22:22:41
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Computational Imaging and Analytics in Biomedical Engineering: Algorithms and ApplicationsНазвание: Computational Imaging and Analytics in Biomedical Engineering: Algorithms and Applications
Автор: Т.R. Gаnеsh Ваbu, U. Sаrаvаnаkumаr, Ваlасhаndrа Раttаnаik
Издательство: Apple Academic Press/CRC Press
Серия: AAP Series on Digital Signal Processing, Computer Vision and Image Processing
Год: 2024
Страниц: 356
Язык: английский
Формат: pdf (true)
Размер: 27.9 MB

This new book focuses on mathematical and numerical methods for medical images and data. The book presents the various mathematical modeling techniques, numerical analysis, computing and computational techniques, and applications of machine learning for medical images and medical informatics. It also focuses on programming concepts using MATLAB and Python for medical image and signal analytics. The volume demonstrates the use of computational techniques and tools such as Machine Learning, deep neural networks, Artificial Intelligence (AI) and human-computer interaction ,fusion methods for CT and pet images, etc., for diagnosis of brain disorders, cervical cancer, lung disease, melanoma, atrial fibrillation and other circulatory issues, dental images, diabetes, and other medical issues. For further simplification and optimization, emerging techniques such as neural networks, Machine Learning, Deep Learning (DL), and Artificial Intelligence (AI) are adapted for feature extraction of medical images, medical image segmen­tation, and image-based bio-models. Image categorization, object identifica­tion, pattern recognition, and reasoning are just a few of the principles that are examined in medical imaging. By extracting meaningful patterns for a specific condition, they are employed in medical imaging to improve accu­racy. They also aid in the process of deciding on a course of action. Using Machine Learning to improve medical imaging is a trend that will continue in the future.

AI for Absolute Beginners: A Clear Guide to Tomorrow

Автор: Limpopo5 от 2024-03-21, 05:17:39
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AI for Absolute Beginners: A Clear Guide to TomorrowНазвание: AI for Absolute Beginners: A Clear Guide to Tomorrow
Автор: Оlivеr Тhеоbаld
Издательство: Scatterplot Press
Серия: AI, Data Science, Python & Statistics for Beginners
Год: 2023
Страниц: 209
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

Curious about AI but feeling lost at the same time? Well, look no further! AI for Absolute Beginners is your guide to demystifying complex AI concepts and providing you with a solid foundation to step into and stay ahead in the new AI era. In this accessible and comprehensive book, we start from scratch, assuming no prior knowledge. Each chapter is carefully crafted to guide you through the fundamental concepts and practical applications of AI, including Machine Learning, generative AI, and Deep Learning. With clear and plain English explanations, you'll be surprised at how quickly you can start thinking and speaking fluently in the words and mindset of an AI expert! From self-driving cars to large language models, the ability of Deep Learning to learn from raw data and process large datasets has dramatically broadened the use cases for Artificial Intelligence (AI). However, like all technologies, Deep Learning has its own unique limitations and challenges that we will examine in this chapter. Before we dive in, it’s important to explain the connection between Deep Learning and Machine Learning. Machine Learning, as discussed, involves algorithms and models that improve with experience and exposure to data. As a subfield of Machine Learning, Deep Learning takes the foundational principles of Machine Learning and applies its own techniques to even larger and more complex datasets. This involves the use of artificial neural networks with deep and multiple layers stacked together to form a model.

Linguistic Resources for Natural Language Processing: On the Necessity of Using Linguistic Methods to Develop NLP Software

Автор: Limpopo5 от 2024-03-17, 16:38:34
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Linguistic Resources for Natural Language Processing: On the Necessity of Using Linguistic Methods to Develop NLP SoftwareНазвание: Linguistic Resources for Natural Language Processing: On the Necessity of Using Linguistic Methods to Develop NLP Software
Автор: Мах Silbеrztеin
Издательство: Springer
Год: 2024
Страниц: 230
Язык: английский
Формат: pdf (true), epub
Размер: 51.0 MB

Empirical ― data-driven, neural network-based, probabilistic, and statistical ― methods seem to be the modern trend. Recently, OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. In consequence, most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars. Not questioning the intrinsic value of many software applications based on empirical methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers.

PyTorch for Natural Language Processing Mastery: Build powerful dialogue models with Python

Автор: Limpopo5 от 2024-03-16, 06:39:53
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PyTorch for Natural Language Processing Mastery: Build powerful dialogue models with PythonНазвание: PyTorch for Natural Language Processing Mastery: Build powerful dialogue models with Python
Автор: Jаmеs Russеl
Издательство: Independently published
Год: 2024
Страниц: 100
Язык: английский
Формат: pdf
Размер: 12.7 MB

Dialogue systems are revolutionizing human-computer interaction, enabling us to interact with machines in a natural and engaging way. But what about open-domain systems? These are the future, capable of extended conversations on any topic, just like you would with a friend. This book empowers you to conquer the world of Natural Language Processing (NLP) with the powerful PyTorch framework. Unleash the ability to build sophisticated dialogue systems, chatbots, and other interactive language applications using Python! For developer eager to harness the power of NLP and PyTorch for building innovative language applications, researcher seeking to delve deeper into the world of open-domain dialogue systems and explore new possibilities, student passionate about NLP and ready to solidify your understanding with practical application, anyone fascinated by the future of AI and human-computer interaction, eager to unlock the potential of dialogue systems.

Data Analytics for Discourse Analysis with Python: The Case of Therapy Talk

Автор: Limpopo5 от 2024-02-25, 16:14:08
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Data Analytics for Discourse Analysis with Python: The Case of Therapy TalkНазвание: Data Analytics for Discourse Analysis with Python: The Case of Therapy Talk
Автор: Dеnnis Тау
Издательство: Routledge
Год: 2024
Страниц: 190
Язык: английский
Формат: pdf (true)
Размер: 16.5 MB

This concise volume, using examples of psychotherapy talk, showcases the potential applications of data analytics for advancing discourse research and other related disciplines. The book provides a brief primer on data analytics, defined as the science of analyzing raw data to reveal new insights and support decision making. Currently underutilized in discourse research, Tay draws on the case of psychotherapy talk, in which clients’ concerns are worked through via verbal interaction with therapists, to demonstrate how data analytics can address both practical and theoretical concerns. The Python code used to implement examples in the conceptual walkthroughs and case studies will be presented as they are discussed, for readers to try them out as they go along. The various code snippets throughout will be presented again at the end of each chapter to provide a bird’s-eye view of the logical flow of the techniques and their components. All code and data files are also available upon request and stored on my GitHub site. The numerical representation of different aspects of texts to facilitate subsequent quantitative analysis is a basic task in fields like corpus linguistics and natural language processing (NLP).

Conversational Artificial Intelligence

Автор: Limpopo5 от 2024-02-20, 07:09:36
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Conversational Artificial IntelligenceНазвание: Conversational Artificial Intelligence
Автор: Rоmil Rаwаt, Rаjеsh Кumаr Сhаkrаwаrti, Sаnjаyа Кumаr Sаrаngi
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 774
Язык: английский
Формат: pdf (true)
Размер: 12.9 MB

Conversational AI combines Natural Language Processing (NLP) with traditional software like chatbots, voice assistants, or an interactive voice recognition system to help customers through either a spoken or typed interface. Conversational chatbots that respond to questions promptly and accurately to help customers are a fascinating development since they make the customer service industry somewhat self-sufficient. A well-automated chatbot can decimate staffing needs, but creating one is a time-consuming process. Voice recognition technologies are becoming more critical as AI assistants like Alexa become more popular. Chatbots in the corporate world have advanced technical connections with clients thanks to improvements in Artificial Intelligence. However, these chatbots’ increased access to sensitive information has raised serious security concerns. Threats are one-time events such as malware and DDOS (Distributed Denial of Service) assaults. Targeted strikes on companies are familiar and frequently lock workers out. User privacy violations are becoming more common, emphasizing the dangers of employing chatbots. Vulnerabilities are systemic problems that enable thieves to break in. Vulnerabilities allow threats to enter the system, hence they are inextricably linked.

Handbook of Artificial Intelligence and Wearables: Applications and Case Studies

Автор: Limpopo5 от 2024-02-15, 04:37:42
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Handbook of Artificial Intelligence and Wearables: Applications and Case StudiesНазвание: Handbook of Artificial Intelligence and Wearables: Applications and Case Studies
Автор: Неmасhаndrаn К, Маnjееt Rеgе, Zitа Zоltау Рарrikа, К. V. Rаjеsh Кumаr
Издательство: CRC Press
Год: 2024
Страниц: 409
Язык: английский
Формат: pdf (true)
Размер: 23.4 MB

The ever-changing world of wearable technologies makes it difficult for experts and practitioners to keep up with the most recent developments. This handbook provides a solid understanding of the significant role that AI plays in the design and development of wearable technologies along with applications and case studies. Handbook of Artificial Intelligence and Wearables: Applications and Case Studies presents a deep understanding of AI and its involvement in wearable technologies. The book discusses the key role that AI plays and goes on to discuss the challenges and possible solutions. It highlights the more recent advances along with real-world approaches for the design and development of the most popular AI-enabled wearable devices such as smart fitness trackers, AI-enabled glasses, sports wearables, disease diagnostic devices, and more, complete with case studies. Integration with Artificial Intelligence (AI) and Machine Learning (ML) in smart glasses opens up a wide range of possibilities and applications that will enhance functionality and take the capabilities of these wearable devices to the next level. AI algorithms can be used to enable object and image recognition capabilities in smart glasses. This will allow users to identify and gather information about the objects that are in sight of them, it can also detect landmarks, people, or text simply by looking at them.

Introduction to Generative AI: An Ethical, Societal, and Legal Overview (Final Release)

Автор: Limpopo5 от 2024-02-12, 22:14:57
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Introduction to Generative AI: An Ethical, Societal, and Legal Overview (Final Release)Название: Introduction to Generative AI: An Ethical, Societal, and Legal Overview (Final Release)
Автор: Numа Dhаmаni, Маggiе Еnglеr
Издательство: Manning Publications
Год: 2024
Страниц: 336
Язык: английский
Формат: epub (true)
Размер: 10.1 MB

Generative AI tools like ChatGPT are amazing—but how will their use impact our society? This book introduces the world-transforming technology and the strategies you need to use generative AI safely and effectively. Introduction to Generative AI gives you the hows-and-whys of generative AI in accessible language. Anyone who uses ChatGPT for even a few minutes can tell that it’s truly different from other chatbots or question-and-answer tools. Introduction to Generative AI guides you from that first eye-opening interaction to how these powerful tools can transform your personal and professional life. In it, you’ll get no-nonsense guidance on generative AI fundamentals to help you understand what these models are (and aren’t) capable of, and how you can use them to your greatest advantage. Generative AI tools like ChatGPT, Bing, and Bard have permanently transformed the way we work, learn, and communicate. This delightful book shows you exactly how Generative AI works in plain, jargon-free English, along with the insights you’ll need to use it safely and effectively. This book is written for anyone who has an interest in generative AI technology and wants to understand how to be a responsible participant in this area of innovation. While basic exposure to Machine Learning and natural language processing (NLP) concepts is helpful, it’s not required.

Deep Learning for Natural Language Processing: A Gentle Introduction

Автор: Limpopo5 от 2024-02-10, 00:32:32
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Deep Learning for Natural Language Processing: A Gentle IntroductionНазвание: Deep Learning for Natural Language Processing: A Gentle Introduction
Автор: Мihаi Surdеаnu, Маrсо Аntоniо Vаlеnzuеlа-Еsсаrсеgа
Издательство: Cambridge University Press
Год: 2024
Страниц: 345
Язык: английский
Формат: pdf (true)
Размер: 83.1 MB

Deep Learning is becoming increasingly important in a technology-dominated world. However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics. This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to Deep Learning for Natural Language Processing (NLP). It covers both theoretical and practical aspects, and assumes minimal knowledge of Machine Learning, explaining the theory behind natural language in an easy-to-read way. It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern Deep Learning libraries such as PyTorch and Hugging Face. Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems.

A Course in Natural Language Processing

Автор: Limpopo5 от 2024-02-01, 15:54:53
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A Course in Natural Language ProcessingНазвание: A Course in Natural Language Processing
Автор: Yаnnis Наrаlаmbоus
Издательство: Springer
Год: 2024
Страниц: 543
Язык: английский
Формат: pdf (true)
Размер: 21.4 MB

Natural Language Processing is the branch of Artificial Intelligence involving language, be it in spoken or written modality. Teaching Natural Language Processing (NLP) is difficult because of its inherent connections with other disciplines, such as Linguistics, Cognitive Science, Knowledge Representation, Machine Learning, Data Science, and its latest avatar: Deep Learning. Most introductory NLP books favor one of these disciplines at the expense of others. Based on a course on Natural Language Processing taught by the author at IMT Atlantique for over a decade, this textbook considers three points of view corresponding to three different disciplines, while granting equal importance to each of them. As such, the book provides a thorough introduction to the topic following three main threads: the fundamental notions of Linguistics, symbolic Artificial Intelligence methods (based on knowledge representation languages), and statistical methods (involving both legacy machine learning and deep learning tools). Complementary to this introductory text is teaching material, such as exercises and labs with hints and expected results. Complete solutions with Python code are provided for educators on the SpringerLink webpage of the book.

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