Название: Modern Data Mining with Python: A risk-managed approach to developing and deploying explainable and efficient algorithms using ModelOps
Автор: Dushуаnt Singh Sеngаr, Vikаsh Сhаndrа
Издательство: BPB Publications
Год: 2024
Страниц: 438
Язык: английский
Формат: epub (true)
Размер: 20.0 MB

"Modern Data Mining with Python" is a guidebook for responsibly implementing data mining techniques that involve collecting, storing, and analyzing large amounts of structured and unstructured data to extract useful insights and patterns. Enter into the world of data mining and Machine Learning. Use insights from various data sources, from social media to credit card transactions. Master statistical tools, explore data trends, and patterns. Understand decision trees and artificial neural networks (ANNs). Manage high-dimensional data with dimensionality reduction. Explore binary classification with logistic regression. Spot concealed patterns with unsupervised learning. Analyze text with recurrent neural networks (RNNs) and visuals with convolutional neural networks (CNNs). Ensure model compliance with regulatory standards. The book starts from the basics of statistics and exploratory data analysis and then ventures into advanced Deep Learning techniques. It emphasizes ethical Machine Learning model development, tackling biases, ensuring algorithmic transparency, and adhering to responsible AI principles. This approach is not only about learning techniques but also about becoming a responsible decision-maker in the data-driven business world. After reading this book, readers will be equipped with the skills and knowledge necessary to use Python for data mining and analysis in an industry set-up. They will be able to analyze and implement algorithms on large structured and unstructured datasets.

Название: Mastering Large Language Models with Python: Unleash the Power of Advanced Natural Language Processing for Enterprise Innovation and Efficiency Using Large Language Models (LLMs) with Python
Автор: Rаj Аrun R
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 566
Язык: английский
Формат: pdf, epub (true)
Размер: 10.9 MB

"Mastering Large Language Models with Python" is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of Artificial Intelligence. By the conclusion of this book, readers are not just familiarized with the theoretical underpinnings of LLMs but are also equipped with the hands-on experience necessary to implement these models in practical scenarios. Tailored for AI researchers, industry professionals, and academic students, this book serves as a comprehensive guide to navigating the promising yet complex world of Large Language Models.

Название: Deep Neural Networks-Enabled Intelligent Fault Diagnosis of Mechanical Systems
Автор: Ruqiаng Yаn, Zhibin Zhао
Издательство: CRC Press
Год: 2024
Страниц: 217
Язык: английский
Формат: pdf (true)
Размер: 15.5 MB

The book aims to highlight the potential of Deep Learning (DL)-enabled methods in intelligent fault diagnosis (IFD), along with their benefits and contributions. The authors first introduce basic applications of DL-enabled IFD, including auto-encoders, deep belief networks, and convolutional neural networks. Advanced topics of DL-enabled IFD are also explored, such as data augmentation, multi-sensor fusion, unsupervised deep transfer learning, neural architecture search, self-supervised learning, and reinforcement learning. Aiming to revolutionize the nature of IFD, Deep Neural Networks-Enabled Intelligent Fault Diangosis of Mechanical Systems contributes to improved efficiency, safety, and reliability of mechanical systems in various industrial domains. In recent years, due to the rapid development of computer technology, modern testing technology, and signal processing technology, equipment fault diagnosis technology has made great progress. With the rapid development of artificial intelligence technology, the application of deep neural network (DNN) in intelligent fault diagnosis (IFD) of mechanical systems has further deepened. Deep Learning (DL) is one of the hottest technologies in the current field of Machine Learning. DL is essentially a DNN with multiple hidden layers, and the main difference between it and the traditional multi-layer perceptron is the difference in the learning algorithm. Due to its strong representation learning ability, DL is well-suited for data analysis and classification. Therefore, in the field of intelligent diagnosis, many researchers have applied DL-based techniques, such as multi-layer perceptron (MLP), autoencoder (AE), convolutional neural networks (CNNs), deep belief networks (DBNs), and recurrent neural networks (RNNs), to boost the performance.

Название: Advanced Machine Learning with Evolutionary and Metaheuristic Techniques
Автор: Jауаrаmаn Vаlаdi, Кrishnа Рrаtар Singh, Мunееndrа Оjhа
Издательство: Springer
Год: 2024
Страниц: 365
Язык: английский
Формат: pdf (true), epub
Размер: 50.0 MB

This book delves into practical implementation of evolutionary and metaheuristic algorithms to advance the capacity of Machine Learning. The readers can gain insight into the capabilities of data-driven evolutionary optimization in materials mechanics, and optimize your learning algorithms for maximum efficiency. Or unlock the strategies behind hyperparameter optimization to enhance your transfer learning algorithms, yielding remarkable outcomes. Or embark on an illuminating journey through evolutionary techniques designed for constructing deep-learning frameworks. The book also introduces an intelligent RPL attack detection system tailored for IoT networks. Explore a promising avenue of optimization by fusing Particle Swarm Optimization with Reinforcement Learning.

Название: 5G Edge Computing: Technologies, Applications and Future Visions
Автор: Хiао Ма, Меngwеi Хu, Yuаnzhе Li
Издательство: Springer
Год: 2024
Страниц: 209
Язык: английский
Формат: pdf (true), epub
Размер: 29.5 MB

Edge computing has been identified as one of the key technologies for 5G networks and beyond due to two prominent advantages: low network latency and reduced core network load. By empowering cloud capabilities and IT service environments at the network edge, edge computing can well support applications of 5G and beyond, such as augmented/virtual reality (AR/VR), vehicular network (ultra-reliable low-latency communication services),Internet of Things (massive machine type communication services), and mobile high-definition video (enhanced mobile broadband services). Therefore, edge computing has attracted the attention of both industry and academia since its emergence. In specific, the book is important for the research community for the following four reasons. First, we present the first comprehensive measurement study on a leading public edge platform, and critical concerns on edge computing are studied in depth through passive-active integrated measurements (Chaps. 2–3). The dataset is open-sourced, which will benefit researchers and practitioners of edge computing and cloud computing significantly. Second, following the measurement results, several key technologies of 5G edge computing are investigated to improve the quality of experience for end users, and optimize the system performance/minimize system cost for edge service providers (Chaps. 4–7). Third, this book explores the integration of edge computing with 5G networks. We implement an end-to-end edge computing system following the 5G standard 3GPP TS 23.501, which supports edge functions such as edge service migration (Ch. 8). Fourth, this book discusses visions of edge computing in the future 6G networks and presents our pioneering exploration (both research and practice) toward edge computing in the 6G era (Ch. 9).

Название: Machine Learning for Physics and Astronomy
Автор: Viviаnа Асquаvivа
Издательство: Princeton University Press
Год: 2023
Страниц: 281
Язык: английский
Формат: pdf (true)
Размер: 61.0 MB

A hands-on introduction to Machine Learning and its applications to the physical sciences. As the size and complexity of data continue to grow exponentially across the physical sciences, Machine Learning is helping scientists to sift through and analyze this information while driving breathtaking advances in quantum physics, astronomy, cosmology, and beyond. This incisive textbook covers the basics of building, diagnosing, optimizing, and deploying Machine Learning methods to solve research problems in physics and astronomy, with an emphasis on critical thinking and the scientific method. Using a hands-on approach to learning, Machine Learning for Physics and Astronomy draws on real-world, publicly available data as well as examples taken directly from the frontiers of research, from identifying galaxy morphology from images to identifying the signature of standard model particles in simulations at the Large Hadron Collider. What Is Machine Learning? To the best of my knowledge/ability to explain, I would say that it’s the process of teaching a machine to make informed, data-driven decisions. Examples of such decisions include recognizing and characterizing objects based on similarities or differences, detecting patterns, and distinguishing signal from noise. Each chapter is accompanied by Jupyter Notebook worksheets in Python that enable students to explore key concepts.

Название: The AI Revolution in Customer Service and Support (Early Release)
Автор: Rоss Smith, Еmilу МсКеоn, Мауtе Gоnzаlеz
Издательство: Addison-Wesley Professional/Pearson Education
Год: 2024
Страниц: 409
Язык: английский
Формат: pdf, epub
Размер: 10.3 MB

Generative AI has made amazing advances in the last year, and customer service and support is one of the most important areas where this new technology can have an immediate impact. While the technology is not yet in a place where it will fully replace agents and support engineers, it can do wonders to dramatically improve customer experience while also contribute to the optimization of productivity in various ways. This book help readers understand how and where to incorporate AI technology into the flow of the customer experience. In the 2020s and beyond, AI is entering a new frontier of generative technologies, which aim to create novel and realistic content, such as images, texts, sounds, and videos. Generative technologies use deep learning models, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and large language models (LLMs), to generate content that is indistinguishable from human-produced content. Generative technologies have various applications, such as art, entertainment, education, and communication. One of the most exciting and challenging areas of generative technologies is natural language generation (NLG), which generates natural language text from a given input, such as an image, a keyword, or a prompt. Some of the most notable and influential LLMs include GPTs, BERT, XLNet, T5, and DALL-E, which have been developed and released by leading research labs and companies, such as OpenAI, Google, Facebook, and Microsoft. LLMs have also enabled and inspired the creation and innovation of various applications and products, such as chatbots, assistants, recommender systems, content generators, summarizers, translators, analyzers, or synthesizers, which have been deployed and adopted by various industries and sectors, such as education, health, business, media, entertainment, or art, among others.

Название: Search Methods in Artificial Intelligence
Автор: Dеераk Кhеmаni
Издательство: Cambridge University Press
Год: 2024
Страниц: 488
Язык: английский
Формат: pdf
Размер: 11.3 MB

This book is designed to provide in-depth knowledge on how search plays a fundamental role in problem solving. Meant for undergraduate and graduate students pursuing courses in Computer Science and Artificial Intelligence, it covers a wide spectrum of search methods. Readers will be able to begin with simple approaches and gradually progress to more complex algorithms applied to a variety of problems. It demonstrates that search is all pervasive in Artificial Intelligence and equips the reader with the relevant skills. This book is meant for the serious practitioner-to-be of constructing intelligent machines. Machines that are aware of the world around them, that have goals to achieve, and the ability to imagine the future and make appropriate choices to achieve those goals. It is an introduction to a fundamental building block of Artificial Intelligence (AI). As the book shows, search is central to intelligence. A neuron is a simple device that computes a simple function of the inputs it receives. Collections of interconnected neurons can do complex computations. Insights into animal brains have prompted many researchers to pursue the path of creating artificial neural networks (ANNs). An ANN is a computational model that can be trained to perform certain tasks by repeatedly showing a stimulus and the expected response. Deep networks got further impetus with the availability of open source software like Tensorflow from Google that makes the task of implementing Machine Learning models easier for researchers. More recently, generative neural networks have been successfully deployed for language generation and even creating paintings, for example, from OpenAI. Generative models embody a form of unsupervised learning from large amounts of data, and are then trained to generate data like the one the algorithms were trained on.

Название: AI-Driven Cybersecurity and Threat Intelligence: Cyber Automation, Intelligent Decision-Making and Explainability
Автор: Iqbаl Н. Sаrkеr
Издательство: Springer
Год: 2024
Страниц: 207
Язык: английский
Формат: pdf (true), epub
Размер: 21.0 MB

This book explores the dynamics of how AI (Artificial Intelligence) technology intersects with cybersecurity challenges and threat intelligence as they evolve. Integrating AI into cybersecurity not only offers enhanced defense mechanisms, but this book introduces a paradigm shift illustrating how one conceptualize, detect and mitigate cyber threats. An in-depth exploration of AI-driven solutions is presented, including machine learning algorithms, data science modeling, generative AI modeling, threat intelligence frameworks and Explainable AI (XAI) models. As a roadmap or comprehensive guide to leveraging AI/XAI to defend digital ecosystems against evolving cyber threats, this book provides insights, modeling, real-world applications and research issues. Throughout this journey, the authors discover innovation, challenges, and opportunities. It provides a holistic perspective on the transformative role of AI in securing the digital world. This book targets advanced-level students in Computer Science as a secondary textbook. Researchers and industry professionals working in various areas, such as Cyber AI, Explainable and Responsible AI, Human-AI Collaboration, Automation and Intelligent Systems, Adaptive and Robust Security Systems, Cybersecurity Data Science and Data-Driven Decision Making will also find this book useful as reference book.

Название: 3D Computer Vision: Foundations and Advanced Methodologies
Автор: Yu-Jin Zhаng
Издательство: Springer/Publishing House of Electronics Industry
Год: 2024
Страниц: 479
Язык: английский
Формат: pdf
Размер: 10.9 MB

This book offers a comprehensive and unbiased introduction to 3D Computer Vision, ranging from its foundations and essential principles to advanced methodologies and technologies. Divided into 11 chapters, it covers the main workflow of 3D computer vision as follows: camera imaging and calibration models; various modes and means of 3D image acquisition; binocular, trinocular and multi-ocular stereo vision matching techniques; monocular single-image and multi-image scene restoration methods; point cloud data processing and modeling; simultaneous location and mapping; generalized image and scene matching; and understanding spatial-temporal behavior. Computer Vision is the use of computers to realize human visual functions, that is, the sensation, perception, processing, and interpretation of three-dimensional scenes in the objective world. The original purpose of vision research is to grasp and understand the image of the scene; identify and locate the objects in it; determine their own structure, spatial arrangement, and distribution; and explain the relationship between objects. The research goal of computer vision is to make meaningful judgments about actual objects and scenes in the objective world based on perceived images. Deep Learning uses cascaded multilayer nonlinear processing units for feature extraction and transformation, realizing multilevel feature representation and concept abstraction learning. Deep Learning still belongs to the category of Machine Learning, but compared with traditional machine learning methods, Deep Learning methods avoid the requirements for manual design features under traditional Machine Learning methods and show obvious effect advantages under Big Data.