Название: Prometheus: Up & Running: Infrastructure and Application Performance Monitoring, 2nd Edition (Final Release)
Автор: Julien Pivotto, Brian Brazil
Издательство: O’Reilly Media, Inc.
Год: 2023
Страниц: 415
Язык: английский
Формат: epub (true)
Размер: 10.2 MB

Get up to speed with Prometheus, the metrics-based monitoring system used in production by tens of thousands of organizations. This updated second edition provides site reliability engineers, Kubernetes administrators, and software developers with a hands-on introduction to the most important aspects of Prometheus, including dashboarding and alerting, direct code instrumentation, and metric collection from third-party systems with exporters. Prometheus is primarily written in Go and licensed under the Apache 2.0 license. For instrumenting your own code, there are client libraries in all the popular languages and runtimes, including Go, Java/JVM, C#/.Net, Python, Ruby, Node.js, Haskell, Erlang, and Rust. Many popular applications are already instrumented with Prometheus client libraries, like Kubernetes, Docker, Envoy, and Vault. For third-party software that exposes metrics in a non-Prometheus format, there are hundreds of integrations available. These are called exporters, and include HAProxy, MySQL, PostgreSQL, Redis, JMX, SNMP, Consul, and Kafka.

Название: Parallel Programming: for Multicore and Cluster Systems, 3rd Edition
Автор: Thomas Rauber, Gudula Runger
Издательство: Springer
Год: 2023
Страниц: 563
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB

This textbook covers the new development in processor architecture and parallel hardware. It provides detailed descriptions of parallel programming techniques that are necessary for developing efficient programs for multicore processors as well as for parallel cluster systems and supercomputers. The content of the book consists of three main parts, covering all areas of parallel computing: the architecture of parallel systems, parallel programming models and environments, and the implementation of efficient application algorithms. The emphasis lies on parallel programming techniques needed for different architectures. The first part contains an overview of the architecture of parallel systems, including cache and memory organization, interconnection networks, routing and switching techniques as well as technologies that are relevant for modern and future multicore processors. The second part presents parallel programming models, performance models, and parallel programming environments for message passing and shared memory models, including the message passing interface (MPI), Pthreads, Java threads, and OpenMP. For each of these parallel programming environments, the book introduces basic concepts as well as more advanced programming methods and enables the reader to write and run semantically correct and computationally efficient parallel programs.

Название: Human-Centered Artificial Intelligence: Advanced Lectures
Автор: Mohamed Chetouani, Virginia Dignum, Paul Lukowicz, Carles Sierra
Издательство: Springer
Серия: Lecture Notes in Artificial Intelligence
Год: 2023
Страниц: 434
Язык: английский
Формат: pdf (true)
Размер: 34.6 MB

As a discipline, human-centered AI (HCAI) aims to create Artificial Intelligence (AI) systems that collaborate with humans, enhancing human capabilities and empowering humans to achieve their goals. That is, the focus amplify and augment rather than displace human abilities. HCAI seeks to preserve human control in a way that ensures artificial intelligence meets our needs while also operating transparently, delivering equitable outcomes, and respecting human rights and ethical standards. Design methods that enable representation of and adherence to values such as privacy protection, autonomy (human in control), and non-discrimination are core to HCAI. These are themes closely connected to some of the most fundamental challenges of AI. Artificial neural networks provide a distributed computing technology that can be trained to approximate any computable function, and have enabled substantial advances in areas such as computer vision, robotics, speech recognition and natural language processing. The future of AI lies in enabling people to collaborate with machines to solve complex problems. This requires good communication, trust, clarity, and understanding, like any efficient collaboration. Explainable AI (XAI) addresses such challenges, and for years different AI communities have studied such topics, leading to different definitions, evaluation protocols, motivations, and results.

Название: MATLAB Statistics and Machine Learning Toolbox User’s Guide (R2023a)
Автор: MathWorks
Издательство: The MathWorks, Inc.
Год: March 2023
Страниц: 11098
Язык: английский
Формат: pdf (true)
Размер: 51.2 MB

Statistics and Machine Learning Toolbox предоставляет функции и приложения для описания, анализа и моделирования данных. Вы можете использовать описательную статистику, визуализацию и кластеризацию для анализа данных, подгонять распределения вероятностей к данным, генерировать случайные числа для симуляций Монте-Карло и выполнять проверку гипотез. Алгоритмы регрессии и классификации позволяют делать выводы из данных и строить прогнозные модели либо интерактивно, используя приложения Classification и Regression Learner, либо программно, используя AutoML. Инструментарий предоставляет алгоритмы контролируемого, полуконтролируемого и неконтролируемого машинного обучения, включая машины опорных векторов (SVM), деревья решений с усилением, k-средние и другие методы кластеризации. Вы можете применять такие методы интерпретации, как графики частичной зависимости и LIME, а также автоматически генерировать код на языке C/C++ для встраивания.

Название: Ensemble Methods for Machine Learning (Final Release)
Автор: Gautam Kunapuli
Издательство: Manning Publications
Год: 2023
Страниц: 354
Язык: английский
Формат: pdf (true)
Размер: 18.7 MB

Ensemble Machine Learning combines the power of multiple Machine Learning approaches, working together to deliver models that are highly performant and highly accurate. Ensemble Machine Learning trains a diverse group of Machine Learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both Data Science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying Ensemble Machine Learning to real-world data, and be ready to build more explainable ML systems. For Python programmers with Machine Learning experience.

Название: BDD in Action, 2nd Edition (Final Release)
Автор: John Ferguson Smart, Jan Molak
Издательство: Manning Publications
Год: 2023
Страниц: 490
Язык: английский
Формат: pdf (true)
Размер: 22.0 MB

Deliver software that does what it’s supposed to do! Behavior-Driven Development guides your software projects to success with collaboration, communication techniques, and concrete requirements you can turn into automated tests. Around half of all software projects fail to deliver on requirements. Behavior-Driven Development (BDD) helps make sure that yours isn’t one of them. Behavior-Driven Development in Action, Second Edition teaches you how to ensure that everyone involved in a software project—from developers to non-technical stakeholders—are in agreement on goals and objectives. It lays out the communication skills, collaborative practices, and useful automation tools that will let you seamlessly succeed with BDD. For all development teams. No experience with BDD required. Examples in Java, jаvascript, and TypeScript can be easily expressed in your chosen language.

Название: Усовершенствованные структуры данных
Автор: Петер Брасс
Издательство: ДМК Пресс
Год: 2023
Страниц: 428
Язык: русский
Формат: pdf
Размер: 11.9 MB

Структура данных (англ. data structure) — программная единица, позволяющая хранить и обрабатывать однотипные и/или логически связанные данные. Для добавления, поиска, изменения и удаления данных структура данных предоставляет некоторый набор функций, составляющих её интерфейс. В книге приводится всесторонний анализ идей и деталей реализации структур данных как важнейшей составляющей прикладных алгоритмов. Обсуждаются не только эффективные способы реализации операций над множествами чисел, интервалов или строк, представленных в виде различных поисковых структур данных – деревьев, множеств интервалов, кусочно-постоянных функций, прямоугольных областей, непересекающихся подмножеств, куч, хеш-таблиц, но и динамизация и персистентность (сохраняемость) структур. Структуры данных впервые рассматриваются не просто как вспомогательный материал для иллюстрации методологии объектно ориентированного программирования, а как ключевой вопрос разработки алгоритмов. Многочисленные примеры кода на языке C и более 500 ссылок на первоисточники делают книгу исключительно ценной.

Название: Conceptualizing Python in Google COLAB: Hands-on Practical Sessions
Автор: Poornima G. Naik, Girish R. Naik, M.B. Patil
Издательство: Shashwat Publication
Год: 2021
Страниц: 329
Язык: английский
Формат: pdf (true)
Размер: 12.9 MB

This book can serve as textbook for post graduates and reference for any computer graduate. It will also provide easy reference for Computer Professionals who wants to begin their career in Machine Learning using Python. Colaboratory, or ‘Colab’ for short, is a product from Google Research. Colab allows anybody to write and execute arbitrary python code through the browser, and is especially well suited to Machine Learning (ML), data analysis and education. If you are exploring Machine Learning but struggling to conduct simulations on enormous datasets, or an expert playing with ML desperate for extra computational power, Google Colab is the perfect solution for you. Google Colab or ‘the Colaboratory’ is a free cloud service hosted by Google to encourage Machine Learning and Artificial Intelligence research, where often the barrier to learning and success is the requirement of tremendous computational power.

Название: CompTIA ITF+ CertMike: Prepare. Practice. Pass the Test! Get Certified!: Exam FC0-U61
Автор: Mike Chapple
Издательство: Sybex
Год: 2023
Страниц: 371
Язык: английский
Формат: pdf (true)
Размер: 50.4 MB

The leading, no-nonsense guide to get you up to speed on the fundamentals of IT. In CompTIA ITF+ CertMike: Prepare. Practice. Pass the Exam! Get Certified! Exam FC0-U61, experienced tech education guru Mike Chapple delivers an essential and efficient guide to acing the ITF+ (IT Fundamentals+) exam administered by CompTIA. In the book, you’ll get access to every objective tested by the foundational exam, including IT concepts and terminology, infrastructure, applications and software, software development, database fundamentals, and security. IT Concepts and Terminology is the first domain of CompTIA’s ITF+ exam. It provides the foundational knowledge that anyone in information technology needs to understand as they begin their careers. Interpreted languages are executed from source code using an interpreter. Interpreted languages may be either scripting languages (such as Perl, R, Python, Ruby, jаvascript, and VBScript) or markup languages (such as HTML and XML). Compiled languages use a compiler to convert source code into executable machine language. Compiled languages include C, C++, Java, Go, Julia, and FORTRAN. Structured Query Language (SQL) is used to interact with relational databases. Assembly language is written to work on a specific hardware processor.

Название: Not with a Bug, But with a Sticker: Attacks on Machine Learning Systems and What To Do About Them
Автор: Ram Shankar Siva Kumar, Hyrum Anderson
Издательство: Wiley
Год: 2023
Страниц: 231
Язык: английский
Формат: pdf (true), epub
Размер: 17.2 MB

A robust and engaging account of the single greatest threat faced by AI and ML systems. In Not With A Bug, But With A Sticker: Attacks on Machine Learning Systems and What To Do About Them, a team of distinguished adversarial Machine Learning researchers deliver a riveting account of the most significant risk to currently deployed Artificial Intelligence systems: cybersecurity threats. The authors take you on a sweeping tour – from inside secretive government organizations to academic workshops at ski chalets to Google’s cafeteria – recounting how major AI systems remain vulnerable to the exploits of bad actors of all stripes. Based on hundreds of interviews of academic researchers, policy makers, business leaders and national security experts, the authors compile the complex science of attacking AI systems with color and flourish and provide a front row seat to those who championed this change. Grounded in real world examples of previous attacks, you will learn how adversaries can upend the reliability of otherwise robust AI systems with straightforward exploits.