Название: Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks
Автор: Christian Rathgeb, Ruben Tolosana
Издательство: Springer
Серия: Advances in Computer Vision and Pattern Recognition
Год: 2022
Страниц: 481
Язык: английский
Формат: pdf (true)
Размер: 13.5 MB

This book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, Machine Learning, media forensics, biometrics, and the general security area.

Название: MLOps Engineering at Scale
Автор: Carl Osipov
Издательство: Manning Publications
Год: 2022
Страниц: 344
Язык: английский
Формат: epub
Размер: 10.2 MB

MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production. To get the most value from this book, you’ll want to have existing skills in data analysis with Python and SQL, as well as have some experience with machine learning. I expect that if you are reading this book, you are interested in developing your expertise as a machine learning engineer, and you are planning to deploy your machine learning—based prototypes to production.

Название: Machine Learning for High-Risk Applications: Techniques for Responsible AI (Fourth Early Release)
Автор: Patrick Hall, James Curtis, Parul Pandey
Издательство: O’Reilly Media, Inc.
Год: 2022-02-08
Страниц: 350
Язык: английский
Формат: epub
Размер: 10.4 MB

The past decade has witnessed a wide adoption of artificial intelligence and machine learning (AI/ML) technologies. However, a lack of oversight into their widespread implementation has resulted in harmful outcomes that could have been avoided with proper oversight. Before we can realize AI/ML's true benefit, practitioners must understand how to mitigate its risks. This book describes responsible AI, a holistic approach for improving AI/ML technology, business processes, and cultural competencies that builds on best practices in risk management, cybersecurity, data privacy, and applied social science.

Название: Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications (Third Early Release)
Автор: Chip Huyen
Издательство: O’Reilly Media, Inc.
Год: 2022-02-03
Страниц: 339
Язык: английский
Формат: epub
Размер: 13.8 MB

Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart. In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. You'll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis.

Название: Differential Evolution: From Theory to Practice
Автор: B. Vinoth K., Diego Oliva, P. N. Suganthan
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2022
Страниц: 389
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

This book addresses and disseminates state-of-the-art research and development of differential evolution (DE) and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques. Differential evolution is a population-based meta-heuristic technique for global optimization capable of handling non-differentiable, non-linear and multi-modal objective functions. Many advances have been made recently in differential evolution, from theory to applications. This book comprises contributions which include theoretical developments in DE, performance comparisons of DE, hybrid DE approaches, parallel and distributed DE for multi-objective optimization, software implementations, and real-world applications. The book is useful for researchers, practitioners, and students in disciplines such as optimization, heuristics, operations research and natural computing.

Название: The Statistical Physics of Data Assimilation and Machine Learning
Автор: Henry D. I. Abarbanel
Издательство: Cambridge University Press
Год: 2022
Страниц: 207
Язык: английский
Формат: pdf (true)
Размер: 138.8 MB

Data assimilation is a hugely important mathematical technique, relevant in fields as diverse as geophysics, Data Science, and neuroscience. This modern book provides an authoritative treatment of the field as it relates to several scientific disciplines, with a particular emphasis on recent developments from Machine Learning (ML) and its role in the optimisation of data assimilation. Underlying theory from statistical physics, such as path integrals and Monte Carlo methods, are developed in the text as a basis for data assimilation, and the author then explores examples from current multidisciplinary research such as the modelling of shallow water systems, ocean dynamics, and neuronal dynamics in the avian brain. The theory of data assimilation and Machine Learning is introduced in an accessible and unified manner, and the book is suitable for undergraduate and graduate students from science and engineering without specialized experience of statistical physics.

Название: Advanced Data Mining Tools and Methods for Social Computing
Автор: Sourav De, Sandip Dey
Издательство: Academic Press, Elsevier
Год: 2022
Страниц: 292
Язык: английский
Формат: pdf, epub
Размер: 10.2 MB

Advanced Data Mining Tools and Methods for Social Computing explores advances in the latest data mining tools, methods, algorithms and the architectures being developed specifically for social computing and social network analysis. The book reviews major emerging trends in technology that are supporting current advancements in social networks, including data mining techniques and tools. It also aims to highlight the advancement of conventional approaches in the field of social networking. Chapter coverage includes reviews of novel techniques and state-of-the-art advances in the area of data mining, Machine Learning, soft computing techniques, and their applications in the field of social network analysis.

Название: Machine Learning and Optimization Models for Optimization in Cloud
Автор: Punit Gupta, Mayank K. Goyal
Издательство: CRC Press
Год: 2022
Страниц: 219
Язык: английский
Формат: pdf (true)
Размер: 15.1 MB

Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition.

Название: Machine Learning: Algorithms, Models and Applications
Автор: Jaydip Sen
Издательство: ITexLi
Год: 2021
Страниц: 131
Язык: английский
Формат: pdf (true)
Размер: 10.26 MB

The chapters in the book illustrate how Machine Learning and Deep Learning algorithms and models are designed, optimized, and deployed. The volume will be useful for advanced graduate and doctoral students, researchers, faculty members of universities, practicing data scientists and data engineers, professionals, and consultants working on the broad areas of Machine Learning, Deep Learning, and Artificial Intelligence. Recent times are witnessing rapid development in Machine Learning algorithm systems, especially in reinforcement learning, natural language processing (NLP), computer and robot vision, image processing, speech, and emotional processing and understanding. In tune with the increasing importance and relevance of Machine Learning models, algorithms, and their applications, and with the emergence of more innovative uses–cases of Deep Learning and Artificial Intelligence, the current volume presents a few innovative research works and their applications in real-world, such as stock trading, medical and healthcare systems, and software automation.

Название: Personalized Machine Learning
Автор: Julian McAuley
Издательство: Cambridge University Press
Год: 2022
Страниц: 337
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

Every day we interact with Machine Learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' Machine Learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.