Название: Artificial Intelligence and Internet of Things in Smart Farming
Автор: Моhаmеd Аbdеl-Ваssеt, Ноssаm Наwаsh
Издательство: CRC Press
Год: 2024
Страниц: 315
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book provides a broad overview of the areas of Artificial Intelligence (AI) that can be used for smart farming applications, through either successful engineering or ground-breaking research. Among them, the highlighted tactics are soil management, water management, crop management, livestock management, harvesting, and the integration of Internet of Things (IoT) in smart farming. Artificial Intelligence and Internet of Things in Smart Farming explores different types of smart framing systems for achieving sustainability goals in the real environment. The authors discuss the benefits of smart harvesting systems over traditional harvesting methods, including decreased labor requirements, increased crop yields, increased probabilities of successful harvests, enhanced visibility into crop health, and lower overall harvest and production costs. It explains and describes big data in terms of its potential five dimensions—volume, velocity, variety, veracity, and valuation—within the framework of smart farming. The authors also discuss the recent IoT technologies, such as fifth-generation networks, blockchain, and digital twining, to improve the sustainability and productivity of smart farming systems. DSS can be developed using programming languages like Python or R, leveraging Machine Learning libraries such as Scikit-learn, TensorFlow, or PyTorch for data analysis and model development.

Название: Python Machine Learning A Beginner's Guide to Scikit-Learn: A Hands-On Approach
Автор: Rаjеndеr Кumаr
Издательство: Jamba Academy
Год: 2023
Страниц: 623
Язык: английский
Формат: epub (true)
Размер: 10.1 MB

Are you ready to dive into the world of Python Machine Learning? Look no further! "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is the perfect guide for you. Written by experienced data scientist, Rajender Kumar, this book takes you on a journey through the basics of Machine Learning and the powerful Scikit-learn library. See how Machine Learning is being used to solve problems in industries such as healthcare, finance and more. The book "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is a comprehensive guide for Machine Learning and Deep Learning concepts using Python. It covers various Machine Learning algorithms and Leep Learning architectures along with hands-on examples to get a better understanding of the concepts. This book is perfect for beginners who are new to Machine Learning and want to learn Scikit-Learn from scratch. It is also ideal for intermediate and advanced users who want to expand their knowledge and build more complex models. The code provided on the Github repository can be downloaded and used freely by readers. The notebooks are organized according to the chapters in the book, making it easier for readers to find the relevant code for each concept.

Название: Building Intelligent Systems Using Machine Learning and Deep Learning: Security, Applications and Its Challenges
Автор: Аbhауа Кumаr Sаhоо, Сhittаrаnjаn Рrаdhаn, Вhаbаni Shаnkаr Рrаsаd Мishrа
Издательство: Nova Science Publishers
Год: 2024
Страниц: 238
Язык: английский
Формат: pdf (true)
Размер: 10.8 MB

The primary objective of this book is to provide insight into the design and development of the intelligent system. The proposed book volume mainly focuses on a Machine Learning and Deep Learning-based intelligent system that would bring out the latest trends in the field of tourism, healthcare, agriculture, etc. This book provides security solutions for the intelligent system in different applications. The technological gaps between the traditional system and intelligent system are mentioned in the book, which will help in better understanding for the implementation of the intelligent system using Machine Learning (ML) and Deep Learning (DL) approaches. Although ML and DL have made great achievements in intelligent systems, there are still substantial open challenges that have not been fully studied. The main open challenges of using ML and DL in intelligent systems are: (i) Better performance of the system (ii) Time complexity of the jobs running inside an intelligent system (iii) Managing overload tasks (iv) Providing security towards the system. This book will definitely help academicians, researchers and industry people towards the security, design and development of the intelligent system.

Название: System Design. Машинное обучение. Подготовка к сложному интервью
Автор: Aли Aминиaн, Aлeкc Cюй
Издательство: Питер
Год: 2024
Страниц: 320
Язык: русский
Формат: pdf
Размер: 11.2 MB

Собеседования по проектированию систем машинного обучения — самые сложные. Если нужно подготовиться к такому, книга создана специально для вас. Также она поможет всем, кто интересуется проектированием систем машинного обучения (МО), будь то новички или опытные инженеры. Собеседование по проектированию систем МО (ML System Design interview), как правило, обязательно для претендентов на вакансии, связанные с проектированием и реализацией систем МО: инженер данных, дата-сайентист, инженер машинного обучения и т.д. Чтобы успешно пройти собеседование по проектированию систем МО, надо хорошо понимать фундаментальные концепции и методы МО, а также уметь их применять, чтобы решать практические задачи. На собеседовании обычно необходимо продемонстрировать, что вы разбираетесь в пайплайнах данных и конструировании признаков, а также умеете проектировать эффективные системы МО. Возможно, вам еще придется проявить умение выбирать подходящие модели для конкретных задач, настраивать их параметры и оценивать производительность. В принципе, цель собеседования состоит в том, чтобы оценить, насколько хорошо соискатель применяет теоретические знания МО, чтобы проектировать и реализовывать эффективные системы. Многие технические специалисты полагают, что системы МО исчерпываются такими алгоритмами МО, как логистическая регрессия или нейронные сети. Тем не менее реальные системы МО далеко не ограничиваются разработкой моделей.

Название: Machine Learning in Python for Process and Equipment Condition Monitoring, and Predictive Maintenance: From Data to Process Insights
Автор: Аnkur Кumаr, Jеsus Flоrеs-Сеrrillо
Издательство: Leanpub
Год: 2024-01-13
Страниц: 361
Язык: английский
Формат: pdf (true)
Размер: 18.0 MB

This book provides a guided tour of ML techniques utilized in process industry for plant health management. Step-by-step instructions, supported with industrial-scale process datasets, show how to develop ML-based solutions for equipment condition monitoring, plantwide monitoring, and predictive maintenance solutions. This book is designed to help readers quickly gain a working knowledge of machine learning-based techniques that are widely employed for building equipment condition monitoring, plantwide monitoring, and predictive maintenance solutions in process industry. The book covers a broad spectrum of techniques ranging from univariate control charts to deep learning-based prediction of remaining useful life. Consequently, the readers can leverage the concepts learned to build advanced solutions for fault detection, fault diagnosis, and fault prognosis. The application focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers and data scientists. No prior experience with Machine Learning or Python is needed. Undergraduate-level knowledge of basic linear algebra and calculus is assumed.

Название: Machine Learning and Deep Learning in Neuroimaging Data Analysis
Автор: Аnithа S. Рillаi, Вindu Меnоn
Издательство: CRC Press
Год: 2024
Страниц: 133
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Machine Learning (ML) and Deep Learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together Artificial Intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.

Название: Machine Learning in Medical Imaging and Computer Vision
Автор: Аmitа Nаndаl, Liаng Zhоu, Аrvind Dhаkа, Тоdоr Gаnсhеv
Издательство: The Institution of Engineering and Technology
Год: 2024
Страниц: 382
Язык: английский
Формат: pdf (true)
Размер: 21.1 MB

Medical images can highlight differences between healthy tissue and unhealthy tissue and these images can then be assessed by a healthcare professional to identify the stage and spread of a disease so a treatment path can be established. With Machine Learning techniques becoming more prevalent in healthcare, algorithms can be trained to identify healthy or unhealthy tissues and quickly differentiate between the two. Statistical models can be used to process numerous images of the same type in a fraction of the time it would take a human to assess the same quantity, saving time and money in aiding practitioners in their assessment. This edited book discusses feature extraction processes, reviews Deep Learning methods for medical segmentation tasks, outlines optimisation algorithms and regularisation techniques, illustrates image classification and retrieval systems, and highlights text recognition tools, game theory, and the detection of misinformation for improving healthcare provision. Machine Learning in Medical Imaging and Computer Vision provides state of the art research on the integration of new and emerging technologies for the medical imaging processing and analysis fields. This book outlines future directions for increasing the efficiency of conventional imaging models to achieve better performance in diagnoses as well as in the characterization of complex pathological conditions. Medical imaging is increasingly using Machine Learning and computer vision. Deep Learning allows convolutional neural networks (CNNs) to classify, segment, and identify medical images. This has allowed the creation of new tools and apps to aid in illness detection and treatment. This discipline studies deep learning-enabled medical computer vision, Machine Learning for medical image analysis, and personalized medicine using image processing, computer vision, and Machine Learning.

Название: Python AI Programming: Navigating fundamentals of ML, Deep Learning, NLP, and reinforcement learning in practice
Автор: Раtriсk J
Издательство: GitforGits
Год: 2024
Страниц: 295
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

This book aspires young graduates and programmers to become AI engineers and enter the world of Artificial Intelligence (AI) by combining powerful Python programming with Artificial Intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to Machine Learning, where readers learn to implement Python in developing predictive models. The book provides a clear and accessible explanation of Machine Learning, incorporating practical examples and exercises that strengthen understanding. We go deep into Deep Learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing (NLP) is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered. Our adventure starts with a detailed overview of Python's principles, revealing how this language is the ideal toolkit for aspiring AI practitioners. As we progress, the domains of Machine Learning and Deep Learning unveil themselves, illustrating how Python's libraries and frameworks are crucial in pioneering advances in these fields. Each chapter advances your AI learning curve, from the fundamentals of data management to the complexity of neural networks.

Название: Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions
Автор: Rаbi Jау
Издательство: Wiley
Год: 2024
Страниц: 527
Язык: английский
Формат: pdf (true), epub
Размер: 39.3 MB

Embrace emerging AI trends and integrate your operations with cutting-edge solutions. Enterprise AI in the Cloud: A Practical Guide to Deploying End-to-End Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and Machine Learning (ML) into their suite of cloud-based solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate go-to guide. The author shows you how to start an enterprise-wide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and hands-on exercises. You'll also discover best practices on optimizing cloud infrastructure for scalability and automation. You can use out-of-the-box features provided by various cloud ML platforms, data visualization tools such as Tableau and Google Data Studio, and software libraries such as Pandas and Matplotlib in Python. Whether you're a beginner or an experienced AI or MLOps engineer, business or technology leader, or an AI student or enthusiast, this comprehensive resource empowers you to confidently build and use AI models in production, bridging the gap between proof-of-concept projects and real-world AI deployments. With over 300 review questions, 50 hands-on exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a must-read for anyone seeking to accelerate AI transformation across their enterprise.

Название: Machine Learning Theory and Applications: Hands-on Use Cases with Python on Classical and Quantum Machines
Автор: Хаviеr Vаsquеs
Издательство: Wiley
Год: 2024
Страниц: 510
Язык: английский
Формат: pdf (true)
Размер: 38.9 MB

Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries. Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.