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 Название: Modern C++23 QuickStart Pro: Advanced programming including variadic templates, lambdas, async IO, multithreading and thread sync
Автор: Jarek Thalor
Издательство: GitforGits
Год: 2024
Страниц: 289
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB
Learn the latest features of C++23 with Modern C++ 23 QuickStart Pro, the perfect book for experienced developers who want to expand their knowledge and skills. This book takes a hands-on approach, providing rapid learning through real-world examples and scenarios that address complex programming challenges in C++. The book begins by demonstrating the power of variadic templates and how to use them for dynamic function signatures. After becoming familiar with fold expressions for argument handling, you will then explore std::tuple and std::variant for handling heterogeneous data. The book then covers advanced function morphing with parameter packs and shape-shifting lambdas, as well as dynamic programming techniques. It also teaches complex function overloading and high-level thread orchestration using futures, promises, and callables. Next, we'll go over some low-level IO operations, such as controlling IO streams, efficiently handling file descriptors, and directly manipulating files. You will then learn how to optimize memory management with shared, unique, and weak pointers, and how to engineer memory performance with custom allocators and cache-aware programming. You will learn advanced synchronization, including atomic operations, mutexes, locks, and thread pools, as well as lock-free data structures for peak performance. In addition, this book covers optimal integer and floating-point operations, arbitrary precision arithmetic, precise rounding with fixed-point arithmetic, and high-performance computation using math constant integration. | |
Разместил: Ingvar16 4-10-2024, 17:20 | Комментарии: 0 | Подробнее
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 Название: No-Code Data Science: Mastering Advanced Analytics, Machine Learning, and Artificial Intelligence
Автор: David Patrishkoff, Robert E Hoyt
Издательство: Lulu.com
Год: October 16, 2023
Страниц: 872
Язык: английский
Формат: epub
Размер: 30.5 MB
No-Code Data Science is a revolutionary book that democratizes the application of predictive analytics for organizations of all sizes. This first-of-its-kind textbook book is designed to empower readers with the ability to leverage advanced analytics, Machine Learning, and AI without using a programming language, such as Python or R. It’s a comprehensive guide to No-Code Data Science (NCDS) that applies free, no-code, and open-source software with Orange visual programming software, JASP, and BlueSky Statistics. A no-shortcuts approach to ML and AI is applied to maximize the accuracy and application potential of predictive models. The NCDS approach is akin to constructing predictive models with pre-made LEGO bricks (visual programming) versus tediously molding shapes from clay (manual coding). A practical how-to approach to predictive modeling is offered while insisting on the rigor of our disciplined NCDS process. Hands-on data exercises are included in the first eleven chapters. QR code links to educational videos are included in most chapters. Data Science background is first explored, discussing basic definitions and data scientist skill sets. This is followed by chapters on data preparation, wrangling, and data visualization. Predictive analytics is covered in chapters on Machine Learning models and model evaluation. Both supervised and unsupervised learning are included in the discourse. Time series forecasting, survival analysis, and geolocation are covered in separate chapters. Artificial Intelligence (AI) is featured in chapters on image analysis and text mining. | |
Разместил: Ingvar16 4-10-2024, 16:32 | Комментарии: 0 | Подробнее
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 Название: Advanced Techniques in Optimization for Machine Learning and Imaging
Автор: Alessandro Benfenati, Federica Porta, Tatiana Alessandra Bubba, Marco Viola
Издательство: Springer
Год: 2024
Страниц: 173
Язык: английский
Формат: pdf (true), epub
Размер: 55.2 MB
In recent years, non-linear optimization has had a crucial role in the development of modern techniques at the interface of Machine Learning and imaging. The present book is a collection of recent contributions in the field of optimization, either revisiting consolidated ideas to provide formal theoretical guarantees or providing comparative numerical studies for challenging inverse problems in imaging. The covered topics include non-smooth optimisation techniques for model-driven variational regularization, fixed-point continuation algorithms and their theoretical analysis for selection strategies of the regularization parameter for linear inverse problems in imaging, different perspectives on Support Vector Machines trained via Majorization-Minimization methods, generalization of Bayesian statistical frameworks to imaging problems, and creation of benchmark datasets for testing new methods and algorithms. In the past years, Support Vector Machines (SVMs) played a crucial role in the context of Machine Learning, for supervised classification and regression tasks. Even in the Deep Learning era, they can outperform other supervised methods and they are still a popular approach. The paper by A. Benfenati et al. investigates a novel approach by training SVMs via a squared hinge loss functional coupled with sparse-promoting regularization, adopting a Majorization-Minimization method. We perform numerical simulations to compare the performance of most commonly used Langevin Monte Carlo algorithms. The Langevin Monte Carlo (LMC) algorithm (possibly with Metropolis–Hastings adjustment), which is derived from the overdamped Langevin diffusion, has become a popular MCMC method for high-dimensional continuously differentiable distributions since it only requires access to a gradient oracle of the potential of the distribution, which can be computed easily using automatic differentiation softwares such as PyTorch, TensorFlow and JAX. | |
Разместил: Ingvar16 4-10-2024, 15:26 | Комментарии: 0 | Подробнее
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 Название: Terraform Cookbook: Recipes for Codifying Infrastructure (Final Release)
Автор: Kerim Satirli, Taylor Dolezal
Издательство: O’Reilly Media, Inc.
Год: 2025
Страниц: 346
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB
Cloud services and SaaS software permeate every company's IT landscape, requiring a shift from manually provisioned services to a more structured approach, with codification at its core. Terraform provides tools to manage the lifecycle of your IT landscape across thousands of different cloud providers and SaaS platforms. By defining your infrastructure as code you can safely and predictably make changes, modularize crucial building blocks, and create reusable service components. Each recipe in this cookbook addresses a specific problem and prefaces the solution with detailed insights into the "how" and "why". If you're just starting with Terraform and codified infrastructure, this book will help you create a solid foundation, on which you can build for years to come. If you're an advanced user, this guide will help you reaffirm your knowledge and take it to the next level, as you challenge yourself with more complex infrastructure, spread across multiple providers. Terraform is a source-available tool created by HashiCorp that allows you to manage your infrastructure as code (IaC). It provides a simple and consistent way to define, provision, and manage resources across cloud platforms and on-premises environments. With Terraform, you can describe your infrastructure in a declarative language called the HashiCorp configuration language (HCL). This allows you to specify the desired state of your infrastructure rather than having to script the steps to get there. This book is for anyone responsible for creating, managing, or improving infrastructure. That includes DevOps engineers, site reliability engineers, infrastructure developers, system administrators, and even ambitious developers looking to broaden their skills. | |
Разместил: Ingvar16 4-10-2024, 13:27 | Комментарии: 0 | Подробнее
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 Название: Reversible World Of Cellular Automata: Fantastic Phenomena And Computing In Artificial Reversible Universe
Автор: Kenichi Morita
Издательство: World Scientific Publishing
Год: 2025
Страниц: 347
Язык: английский
Формат: pdf (true)
Размер: 29.2 MB
This unique comprehensive compendium provides extensive studies on reversible cellular automata (RCAs), exhibiting a wide range of interesting phenomena similar to the Game-of-Life, a well-known irreversible CA. The useful reference text also introduces innovative approaches to constructing universal computers in a reversible cellular space. Numerous figures are included to illustrate the evolution of RCA configurations, and to elucidate theoretical results on RCAs. Additionally, readers can observe evolution processes of various RCAs on the free CA simulator Golly using supplementary files. A cellular automaton (CA) is an abstract spatiotemporal model of dynamical systems. In this book, we use a 2-dimensional reversible cellular automaton (RCA) as a model of a reversible world. Although it is an artificial universe, it is useful for studying which kinds of phenomena emerge from a simple reversible law. Here, we use the framework of partitioned cellular automata (PCA), in which each cell is divided into several parts and changes its state depending on the neighboring cells’ parts. By this, we can design reversible CAs very easily. Readership: Researchers, professionals, academics, and graduate students in theoretical Computer Science, complex systems and computer engineering. | |
Разместил: Ingvar16 4-10-2024, 12:28 | Комментарии: 0 | Подробнее
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 Название: Deep Learning Demystified: A Step-by-Step Introduction to Neural Networks
Автор: Kilho Shin
Издательство: Independently published
Год: 2024
Язык: английский
Формат: epub
Размер: 11.6 MB
"Deep Learning Demystified: A Step-by-Step Introduction to Neural Networks" is a comprehensive guide designed to make the world of artificial neural networks accessible and engaging. With a focus on simplicity and clarity, this book offers readers an easy-to-follow journey through the fascinating field of deep learning, without requiring an extensive background in mathematics or programming. Ever since the concept of Artificial Intelligence (AI) emerged, humans have dreamed of creating machines that can think and communicate like us. This dream is now becoming a reality through the advancements in AI, particularly with Machine Learning and Deep Learning. At the core of these technologies are Artificial Neural Networks (ANNs), which mimic the structure and function of the human brain to process and learn from data. In this book, you will embark on a journey that begins with the basics of neural networks and perceptrons, and gradually progresses to more advanced concepts and applications. Each chapter is meticulously crafted to build your understanding step-by-step, ensuring you grasp the foundational principles before moving on to complex topics. Each chapter is rich with examples, illustrations, and practical insights, making complex topics understandable and enjoyable. Whether you are a student, a professional, or simply someone with a keen interest in AI, this book is your gateway to understanding and harnessing the power of neural networks. | |
Разместил: Ingvar16 4-10-2024, 04:04 | Комментарии: 0 | Подробнее
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 Название: Modern Artificial Intelligence and Data Science 2024: Tools, Techniques and Systems
Автор: Abdellah Idrissi
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2024
Страниц: 591
Язык: английский
Формат: pdf (true)
Размер: 21.0 MB
This book, through its various chapters presenting recent advances in Modern Artificial Intelligence and Data Science as well as their applications, aims to set up lasting and real applications necessary for both academics and professionals. The field of Data Science, for its part, has seen rapid growth in recent years, and thanks to the advances of AI, this growth has accelerated further. Artificial Intelligence and Data Science are two distinct fields, but they complement each other so well that their combination has become a powerful force for driving innovation and progress across all industries. Data Science provides the tools and techniques necessary to ensure that the data used to train AI systems is reliable, accurate and free of bias. This is more than essential to develop reliable AI systems that can make fair and unbiased decisions. It provides the methods, techniques and tools needed to collect, store, clean and analyze data to identify patterns and extract insights. In Part I, entitled Artificial Intelligence, Machine Learning and Deep Learning, the reader will discover everything that revolves around Machine Learning and Deep Learning. Machine learning, which is a common component of AI and Data Science, encompasses the development of algorithms and systems capable of learning from data, making predictions and/or decisions based on this data and also improve over time. | |
Разместил: Ingvar16 4-10-2024, 03:23 | Комментарии: 0 | Подробнее
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 Название: Hands-On Object-Oriented Programming: Mastering OOP Features for Real-World Software Systems Development
Автор: Anil Kumar Rangisetti
Издательство: Apress
Год: 2024
Страниц: 582
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB
Learn how to easily design and implement a software system using object-oriented programming. Through hands-on activities, this book equips you with the real-world practice needed to master OOP skills that can be applied universally across any programming language. This book is dedicated to bridging the gap between theoretical concepts and practical implementation in software development using OOP. Readers will first understand the importance of OOP in handling software complexities before learning how to model real-world problems through access control, extendible object design, and object relationships. Specifically, the author discusses how to use inheritance, association, and polymorphism concepts for implementing reusable and easy-to-use complex software. More importantly, this book covers design patterns and event-driven programming for learning how to model complex interactions among various objects of the software system to implement various use cases. Unlike existing books on OOP which teach syntax and semantics through standard examples, this book leverages an immersive learning experience through a variety of real-world use cases, applications, games, and system software. While written in C++, the skills can be applied to any OOP language of the reader's preference. The book's last chapters will quickly introduce Python and Solidity languages for learning advanced technologies. By the end, novice and seasoned developers will know how to implement practical and reusable software systems with confidence. For beginners and intermediate software developers with basic programming knowledge to quickly learn OOP concepts with C++. | |
Разместил: Ingvar16 3-10-2024, 19:36 | Комментарии: 0 | Подробнее
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Название: UML для простых смертных
Автор: Роберт Максимчук, Эрик Нейбург
Издательство: Лори
Год: 2024
Формат: PDF
Страниц: 302
Размер: 33 Mb
Язык: Русский
"UML для простых смертных" является прекрасным руководством, в котором показываю преимущества применения UML. Эта книга знакомит читателя с различными типами диаграмм и методиками моделирования; в ней приводятся случаи из реальной жизни, которые объясняют, каким образом UML может помочь вам и вашей команде. Книга станет очень ценной для любого, кто управляет организациями, проектами или командами или хочет стать таким.
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Разместил: Chipa 3-10-2024, 19:36 | Комментарии: 0 | Подробнее
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 Название: Machine Learning Production Systems: Engineering Machine Learning Models and Pipelines
Автор: Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 556
Язык: английский
Формат: epub
Размер: 15.6 MB
Using Machine Learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of Machine Learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. If you’re working in ML/AI or if you want to work in ML/AI in any way other than pure research, this book is for you. It’s primarily focused on people who will have a job title of “ML engineer” or something similar, but in many cases, they’ll also be considered data scientists (the difference between the two job descriptions is often murky). On a more fundamental level, this book is for people who need to know about taking ML/AI technologies and using them to create new products and services. Putting models and applications into production might be the main focus of your job, or it might be something that you do occasionally, or it might even be something done by a team you collaborate with. | |
Разместил: Ingvar16 3-10-2024, 16:39 | Комментарии: 0 | Подробнее
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