Название: Secrets of Machine Learning: How It Works and What It Means for You
Автор: Тоm Коhn
Издательство: World Scientific Publishing
Год: 2024
Страниц: 239
Язык: английский
Формат: pdf (true)
Размер: 10.5 MB

Cutting through the mass of technical literature on Machine Learning and AI and the plethora of fear-mongering books on the rise of killer robots, Secrets of Machine Learning offers a clear-sighted explanation for the informed reader of what this new technology is, what it does, how it works, and why it's so important.The surge in computer processing power along with the sheer quantities of training data available, means Machine Learning is now possible in ways wholly unthinkable just five years ago. Computers can recognize potential lung cancer better than doctors, detect fraud better than bankers, and create fake video almost impossible to tell from the real thing. And next, they are likely to drive our cars. Journalist and news product manager Tom Kohn gets to the heart of the revolutionary new technology that is developing all around us, explaining with precision how the different facets of Machine Learning work, how companies are using it, and why it is permeating all parts of society right now. The book guides readers through the arcane science and jargon in a clear and understandable way, but is detailed enough that it doesn't gloss over the hard technical concepts. If you want to know why Siri sometimes misunderstands you, how Netflix recommends your movies, and how Machine Learning will affect your job -- read this book. Anyone curious about how technology is changing in the workplace and the economy will benefit from learning how Machine Learning is filtering into different areas. Employees looking to future-proof their own skills, students seeking to get ahead as they enter the job market, and anyone with a general interest in cutting-edge industry.

Название: Innovative Machine Learning Applications for Cryptography
Автор: J. Аnithа Ruth, G.V. Vijауаlаkshmi, Р. Visаlаkshi
Издательство: IGI Global
Год: 2024
Страниц: 313
Язык: английский
Формат: pdf (true), epub
Размер: 30.9 MB

Data security is paramount in our modern world, and the symbiotic relationship between Machine Learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine Learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how Machine Learning can automate the process of constructing analytical models, providing a continuous learning mechanism to protect against an ever-increasing influx of data. This book goes beyond theoretical exploration, and provides a comprehensive resource designed to empower academic scholars, specialists, and students in the fields of cryptography, Machine Learning, and network security. Its broad scope encompasses encryption, algorithms, security, and more unconventional topics like Quantum Cryptography, Biological Cryptography, and Neural Cryptography. By examining data patterns and identifying vulnerabilities, it equips its readers with actionable insights and strategies that can protect organizations from the dire consequences of security breaches.

Название: Accountable and Explainable Methods for Complex Reasoning over Text
Автор: Рерa Аtаnаsоvа
Издательство: Springer
Год: 2024
Страниц: 208
Язык: английский
Формат: pdf (true)
Размер: 26.7 MB

This thesis presents research that expands the collective knowledge in the areas of accountability and transparency of Machine Learning (ML) models developed for complex reasoning tasks over text. In particular, the presented results facilitate the analysis of the reasons behind the outputs of ML models and assist in detecting and correcting for potential harms. It presents two new methods for accountable ML models; advances the state of the art with methods generating textual explanations that are further improved to be fluent, easy to read, and to contain logically connected multi-chain arguments; and makes substantial contributions in the area of diagnostics for explainability approaches. All results are empirically tested on complex reasoning tasks over text, including fact checking, question answering, and natural language inference. A major concern with Machine Learning (ML) models is their opacity. They are deployed in an increasing number of applications where they often operate as black boxes that do not provide explanations for their predictions. Among others, the potential harms associated with a lack of understanding of the models’ rationales include privacy violations, adversarial manipulations, and unfair discrimination. In Computer Science, the decision-making process of ML models has been studied by developing accountability and transparency methods. Accountability methods, such as adversarial attacks and diagnostic datasets, expose vulnerabilities in ML models that could lead to malicious manipulations or systematic faults in their predictions.

Название: Methodologies, Frameworks, and Applications of Machine Learning
Автор: Рrаmоd Кumаr Srivаstаvа, Аshоk Кumаr Yаdаv
Издательство: IGI Global
Год: 2024
Страниц: 315
Язык: английский
Формат: pdf (true), epub
Размер: 36.4 MB

In the ever-evolving landscape of technology, Machine Learning stands as a beacon of innovation with the potential to reshape industries and redefine our daily lives. As editors of this comprehensive reference book, Methodologies, Frameworks, and Applications of Machine Learning, we are thrilled to present a compendium that encapsulates the essence of the latest advancements, theoretical foundations, and practical applications in the realm of Machine Learning. Technology is constantly evolving, and Machine Learning is positioned to become a pivotal tool with the power to transform industries and revolutionize everyday life. This book underscores the urgency of leveraging the latest Machine Learning methodologies and theoretical advancements, all while harnessing a wealth of realistic data and affordable computational resources. Machine Learning is no longer confined to theoretical domains; it is now a vital component in healthcare, manufacturing, education, finance, law enforcement, and marketing, ushering in an era of data-driven decision-making. The Chapter 2 focuses on practical implementations of Machine Learning projects using Scikit-learn and TensorFlow libraries in Python. Four distinct projects unfold, each addressing classification, regression, and image classification problems. The step-by-step walkthrough covers model evaluation using classical Machine Learning techniques and deep neural networks.

Название: Robust Machine Learning Distributed Methods for Safe AI
Автор: Rасhid Guеrrаоui, Niruраm Guрta, Rаfаеl Рinоt
Издательство: Springer
Серия: Machine Learning: Foundations, Methodologies, and Applications
Год: 2024
Страниц: 180
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Today, Machine Learning algorithms are often distributed across multiple machines to leverage more computing power and more data. However, the use of a distributed framework entails a variety of security threats. In particular, some of the machines may misbehave and jeopardize the learning procedure. This could, for example, result from hardware and software bugs, data poisoning or a malicious player controlling a subset of the machines. This book explains in simple terms what it means for a distributed Machine Learning scheme to be robust to these threats, and how to build provably robust Machine Learning algorithms. Studying the robustness of Machine Learning algorithms is a necessity given the ubiquity of these algorithms in both the private and public sectors. Accordingly, over the past few years, we have witnessed a rapid growth in the number of articles published on the robustness of distributed Machine Learning algorithms. We believe it is time to provide a clear foundation to this emerging and dynamic field. By gathering the existing knowledge and democratizing the concept of robustness, the book provides the basis for a new generation of reliable and safe Machine Learning schemes. In addition to introducing the problem of robustness in modern Machine Learning algorithms, the book will equip readers with essential skills for designing distributed learning algorithms with enhanced robustness. This book is intended for students, researchers, and practitioners interested in AI systems in general, and in Machine Learning schemes in particular. The book requires certain basic prerequisites in linear algebra, calculus, and probability. Some understanding of computer architectures and networking infrastructures would be helpful.

Название: Reinforcement Learning for Finance: A Python-Based Introduction (Early Release)
Автор: Yvеs J. Нilрisсh
Издательство: O’Reilly Media, Inc.
Год: 2024-03-27
Страниц: 153
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Reinforcement Learning (RL) has led to several breakthroughs in AI. The use of the Q-learning (DQL) algorithm alone has helped people develop agents that play arcade games and board games at a superhuman level. More recently, RL, DQL, and similar methods have gained popularity in publications related to financial research. This book is among the first to explore the use of Reinforcement Learning methods in finance. Author Yves Hilpisch, founder and CEO of The Python Quants, provides the background you need in concise fashion. ML practitioners, financial traders, portfolio managers, strategists, and analysts will focus on the implementation of these algorithms in the form of self-contained Python code and the application to important financial problems. “Bayesian Learning” discusses Bayesian learning as an example of learning through interaction. “Reinforcement Learning” presents breakthroughs in artificial intelligence that were made possible through reinforcement learning. It also describes the major building blocks of reinforcement learning. “Deep Q-Learning” explains the two major characteristics of deep Q-learning which is the most important algorithm for the remainder of the book.

Название: Domain-Specific Computer Architectures for Emerging Applications: Machine Learning and Neural Networks
Автор: Сhао Wаng
Издательство: CRC Press
Год: 2024
Страниц: 417
Язык: английский
Формат: pdf (true)
Размер: 37.9 MB

With the end of Moore’s Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application. DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. In the emerging field of big data, machine learning, data mining, and artificial intelligence algorithms, as the core components of next‑generation applications, have attracted more attention from researchers. Utilizing existing hardware and software means to carry out the design of a new algorithmic architecture has become a hot research topic nowadays. Accelerating new algorithms in the era of big data is very different from the past. Machine Learning is concerned with using data to construct appropriate predictive models to make predictions about unknown data. According to the similarity of the presentation and implementation of Machine Learning algorithms, we can categorize the algorithms such as Bayesian‑based algorithms and neural network‑based algorithms. Of course, the scope of machine learning is so vast that some algorithms are difficult to categorize explicitly into a particular class, and for some classifications, algorithms of the same classification can target different types of problems.

Название: Societal Impacts of Artificial Intelligence and Machine Learning
Автор: Саrlо Liрizzi
Издательство: Springer
Серия: Synthesis Lectures on Computer Science
Год: 2024
Страниц: 160
Язык: английский
Формат: pdf (true), epub
Размер: 22.0 MB

This book goes beyond the current hype of expectations generated by the news on Artificial Intelligence and Machine Learning by analyzing realistic expectations for society, its limitations, and possible future scenarios for the use of this technology in our current society. Artificial Intelligence is one of the top topics today and is inflating expectations beyond what the technology can do in the foreseeable future. The future cannot be predicted, but the future of some elements of our society, such as technology, can be estimated. This book merges the modeling of human reasoning with the power of AI technology allowing readers to make more informed decisions about their personal or financial decisions or just being more educated on current technologies. Algorithms are the root science of AI. With minimal exceptions, all the new algorithms are around more “powerful” versions of what is called “artificial neural networks” (ANN). Artificial Neural Networks are a type of Machine Learning algorithm that is modeled after the structure and function of the human brain. They comprise interconnected nodes, called artificial neurons, that process information and make decisions. ANNs are used to analyze patterns and make predictions from large sets of data.

Название: Data Analytics and Machine Learning: Navigating the Big Data Landscape
Автор: Рushра Singh, Аshа Rаni Мishrа, Рауаl Gаrg
Издательство: Springer
Серия: Studies in Big Data
Год: 2024
Страниц: 357
Язык: английский
Формат: pdf (true)
Размер: 10.7 MB

This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, Big Data, and Machine Learning solutions in their own organizations. The book discusses the transformative power of data analytics and Big Data in various industries and sectors and how Machine Learning applications have revolutionized exploration by enabling advanced data analysis techniques for mapping, geospatial analysis, and environmental monitoring, enhancing our understanding of the world and its dynamic processes. This book explores how Big Data explosion, the power of analytics and Machine Learning revolution can bring new prospects and opportunities in the dynamic and data-rich landscape. It highlights the future research directions in data analytics, Big Data, and Machine Learning that explores the emerging trends, challenges, and opportunities in these fields by covering interdisciplinary approaches such as handling and analyzing real-time and streaming data. The study is done using a popular statistical tool named R programming.

Название: Machine Learning for Complex and Unmanned Systems
Автор: Jоsе Маrtinеz-Саrrаnzа, Еvеrаrdо Inzunzа-Gоnzаlеz, Еnriquе Еfrеn Gаrсіа-Guеrrеrо
Издательство: CRC Press
Год: 2024
Страниц: 386
Язык: английский
Формат: pdf (true)
Размер: 25.4 MB

This book highlights applications that include Machine Learning methods to enhance new developments in complex and unmanned systems. The contents are organized from the applications requiring few methods to the ones combining different methods and discussing their development and hardware/software implementation. The book includes two parts: the first one collects Machine Learning applications in complex systems, mainly discussing developments highlighting their modeling and simulation, and hardware implementation. The second part collects applications of Machine Learning in unmanned systems including optimization and case studies in submarines, drones, and robots. The chapters discuss miscellaneous applications required by both complex and unmanned systems, in the areas of Artificial Intelligence (AI), cryptography, embedded hardware, electronics, the Internet of Things (IoT), and healthcare. Each chapter provides guidelines and details of different methods that can be reproduced in hardware/software and discusses future research.