Название: Building Neural Networks from Scratch with Python Автор: L.D. Кnоwings Издательство: Independently published Год: 2023 Страниц: 163 Язык: английский Формат: pdf Размер: 33.3 MB Ready to throw your hat into the AI and Machine Learning ring? Get started right here, right now! Are you sick of these Machine Learning guides that don’t really teach you anything? Do you already know Python, but you’re looking to expand your horizons and skills with the language? Do you want to dive into the amazing world of neural networks, but it just seems like it’s… not for you? Artificial Intelligence is progressing at a fantastic rate—every day, a new innovation hits the net, providing more and more opportunities for the advancement of society. In your everyday life, your job, and even in your passion projects, learning how to code a neural network can be game-changing. But it just seems… complicated. How do you learn everything that goes into such a complex topic without wanting to tear your own hair out? Well, it just got easier. Machine Learning and neural networking don’t have to be complicated—with the right resources, you can successfully code your very own neural network from scratch, minimal experience needed! In this all-encompassing guide to coding neural networks in Python, you’ll uncover everything you need to go from zero to hero—transforming how you code and the scope of your knowledge right before your eyes. By the end of this book, you’ll have mastered neural networks confidently!
Название: Neural Network Computer Vision with OpenCV 5: Build computer vision solutions using Python and DNN module Автор: Gорi Кrishnа Nuti Издательство: BPB Publications Год: 2024 Страниц: 307 Язык: английский Формат: epub (true) Размер: 31.5 MB Unlocking computer vision with Python and OpenCV. Neural Network Computer Vision with OpenCV equips you with professional skills and knowledge to build intelligent vision systems using OpenCV. It creates a sequential pathway for understanding morphological operations, edge and corner detection, object localization, image classification, segmentation, and advanced applications like face detection and recognition, and optical character recognition. This book offers a practical roadmap to explore the nuances of image processing with detailed discussions on each topic, supported by hands-on Python code examples. The readers will learn the basics of neural networks, Deep Learning and CNNs by using Deep Learning frameworks like Keras, Tensorflow, PyTorch, Caffe etc. They will be able to utilize OpenCV DNN module to classify images by using models like Inception V3, Resnet 101, Mobilenet V2. Moreover, the book will help to successfully Implement object detection using YOLOv3, SSD and R-CNN models. The character detection and recognition models are also covered in depth with code examples. You will gain a deeper understanding of how these techniques impact real-world scenarios and learn to harness the potential of Python and OpenCV to solve complex problems.
Название: Handbook of Face Recognition: The Deep Neural Network Approach, 3rd Edition Автор: Stаn Z. Li, Аnil К. Jаin, Jiаnkаng Dеng Издательство: Springer Год: 2024 Страниц: 473 Язык: английский Формат: pdf (true) Размер: 12.7 MB Over the past decade, Deep Learning has emerged as a powerful tool for solving a wide range of complex problems in Computer Vision, speech recognition, and Natural Language Processing (NLP). One area where Deep Learning has shown particularly promising results is in face recognition. This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational recognition systems. After a thorough introduction, each subsequent chapter focuses on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Face recognition is a critical technology with applications in security, surveillance, biometrics, and human-computer interaction. Deep learning-based approaches have achieved state-of-the-art performance in face recognition tasks, enabling accurate and efficient recognition of faces in a variety of settings. This handbook brings together some of the leading experts in the field of deep learning-based face recognition to provide a comprehensive overview of the current state of the art. This book serves as an all-encompassing resource, providing theoretical underpinnings, algorithms, and implementations to guide students, researchers, and practitioners across all aspects of face recognition. In addition to showcasing the most recent advancements in methods and algorithms, the book also supplies code and data to facilitate hands-on learning and the creation of reproducible face recognition algorithms and systems (Appendix) through Deep Learning programming. The code and data will be accessible on GitHub and will be updated regularly to keep the materials up to date.
Название: Neural Networks for Beginners: Comprehensive Guide to Understanding the Power of Artificial Intelligence Автор: Sаm Саmрbеll Издательство: Independently published Год: 2023 Язык: английский Формат: pdf Размер: 24.9 MB Dive into the foundations of Artificial Intelligence, demystifying the core concept of Neural Networks. From the basics of neurons and synapses to the intricate architecture of these intelligent systems, each chapter unfolds the secrets behind the machines that can learn, adapt, and make decisions on their own. Learn the building blocks of neural networks, from activation functions to the crucial role of data in training these intelligent systems. Gain insights into popular neural network architectures, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), exploring their applications in image recognition, natural language processing, and more. Practicality meets theory as you discover the tools and frameworks powering neural networks, with an introduction to TensorFlow, PyTorch, and the user-friendly Keras. Explore the world of ethical considerations in AI, delving into issues of bias, fairness, and transparency that accompany the development of intelligent systems. Whether you're a student, enthusiast, or professional seeking to grasp the fundamentals, "Neural Networks Unveiled" empowers you with the knowledge to navigate the evolving landscape of Artificial Intelligence. Embark on your journey into the future—where machines learn, adapt, and innovate alongside us. "Neural Networks Unveiled" is your key to unlocking the mysteries of Artificial Intelligence and embracing the limitless possibilities that lie ahead.
Название: AI Applications to Communications and Information Technologies: The Role of Ultra Deep Neural Networks Автор: Dаniеl Мinоli, Веnеdiсt Оссhiоgrоssо Издательство: Wiley-IEEE Press Год: 2024 Страниц: 493 Язык: английский Формат: pdf (true), epub Размер: 26.4 MB Apply the technology of the future to networking and communications. Artificial Intelligence (AI), which enables computers or computer-controlled systems to perform tasks which ordinarily require human-like intelligence and decision-making, has revolutionized computing and digital industries like few other developments in recent history. Tools like artificial neural networks (ANNs), large language models, and Deep Learning have quickly become integral aspects of modern life. With research and development into AI technologies proceeding at lightning speeds, the potential applications of these new technologies are all but limitless. Artificial Intelligence (AI) is a subfield of Computer Science (CS) that focuses on the creation of computer-based systems, applications, and algorithms that mimic, to the degree possible, some cognitive processes intrinsic to human intelligence. The field has had a long history and is now blossoming in an all-encompassing manner. AI technologies, particularly Machine Learning (ML) and Deep Learning (DL), are becoming ubiquitous in nearly all aspects of modern life. DL is a subfield of ML as discussed below. The goals of learning are (i) understanding a process or phenomenon and (ii) making prediction about outcomes, namely, inferring a function or relationship that maps the input to an output in such a manner that the learned relationship can be used to predict the future output from a future input. AI applications, and ML/DL- based systems in particular, are positioned to take over complex tasks generally performed by humans (decision- makers) or to provide added support to people. Siri, Alexa, augmented reality (AR), autonomous driving, and object recognition are just a few examples of AI applications.
Название: Accelerators for Convolutional Neural Networks Автор: Аrslаn Мunir, Jооnhо Коng, Маhmооd Аzhаr Qurеshi Издательство: Wiley-IEEE Press Год: 2024 Страниц: 307 Язык: английский Формат: pdf (true) Размер: 10.1 MB Comprehensive and thorough resource exploring different types of convolutional neural networks and complementary accelerators. Accelerators for Convolutional Neural Networks provides basic deep learning knowledge and instructive content to build up convolutional neural network (CNN) accelerators for the Internet of things (IoT) and edge computing practitioners, elucidating compressive coding for CNNs, presenting a two-step lossless input feature maps compression method, discussing arithmetic coding -based lossless weights compression method and the design of an associated decoding method, describing contemporary sparse CNNs that consider sparsity in both weights and activation maps, and discussing hardware/software co-design and co-scheduling techniques that can lead to better optimization and utilization of the available hardware resources for CNN acceleration. The first part of the book provides an overview of CNNs along with the composition and parameters of different contemporary CNN models.
Название: Прогнозирование: принципы и практика Автор: Poн Xaйндмaн, Джopдж Aтaнacoпyлoc Издательство: ДМК Пресс Год: 2023 Страниц: 459 Язык: русский Формат: pdf Размер: 19.9 MB Данная книга представляет собой всестороннее введение в методы прогнозирования и содержит достаточно информации о каждом из них, помогая читателям разумно их использовать. Примеры с многочисленными наборами данных на языке R авторы заимствовали из собственного опыта консультирования. В конце глав приводятся упражнения по пройденной теме. На протяжении всей книги мы используем язык программирования R и хотим, чтобы студенты научились делать прогнозы с помощью R. Язык R бесплатен и доступен практически в любой операционной системе. Это прекрасный инструмент для любого статистического анализа, а не только для прогнозирования. Инструкции по инсталлированию и использованию R см. в приложении А «Использование языка R». Все примеры на R в книге основаны на допущении, что вы сначала скачали пакет fpp3.
Название: Blockchain Intelligence: Methods, Applications and Challenges Автор: Zibin Zheng, Hong-Ning Dai Издательство: Springer Год: 2021 Страниц: 170 Язык: английский Формат: pdf (true), epub Размер: 23.0 MB This book focuses on using Artificial Intelligence (AI) to improve blockchain ecosystems. Gathering the latest advances resulting from AI in blockchain data analytics, it also presents big data research on blockchain systems. Despite blockchain's merits of decentralisation, immutability, non-repudiation and traceability, the development of blockchain technology has faced a number of challenges, such as the difficulty of data analytics on encrypted blockchain data, poor scalability, software vulnerabilities, and the scarcity of appropriate incentive mechanisms. Combining AI with blockchain has the potential to overcome the limitations, and machine learning-based approaches may help to analyse blockchain data and to identify misbehaviours in blockchain.
Название: Надежность нейронных сетей. Укрепляем устойчивость ИИ к обману Автор: Кэти Уорр Издательство: Питер Год: 2021 Страниц: 272 Язык: русский Формат: pdf Размер: 10.0 MB Глубокие нейронные сети (DNN) становятся неотъемлемой частью IT-продуктов, приводя к появлению нового направления кибератак. Хакеры пытаются обмануть нейросети с помощью данных, которые не смогли бы обмануть человека. Автор рассматривает мотивацию подобных атак, риски, которые влечет вредоносный ввод, а также методы повышения устойчивости ИИ к таким взломам. Если вы специалист в науке о данных, архитектор системы безопасности и стремитесь повысить устойчивость систем с ИИ или вас просто интересует различие между искусственным и биологическим восприятием, то эта книга для вас.
Название: Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults Автор: Nabamita Banerjee Roy and Kesab Bhattacharya Издательство: CRC Press Год: 2022 Страниц: 144 Язык: английский Формат: pdf (true) Размер: 13.3 MB Accurate, fast, and reliable fault classification techniques are an important operational requirement in modern-day power transmission systems. Application of Signal Processing Tools and Neural Network in Diagnosis of Power System Faults examines power system faults and conventional techniques of fault analysis. The authors provide insight into artificial neural networks and their applications, with illustrations, for identifying power system faults. Wavelet transform and its application are discussed as well as an elaborate method of Stockwell transform. The authors also employ probabilistic neural networks (PNN) and back propagation neural networks (BPNN) to identify the different types of faults and determine their corresponding locations, respectively.
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