Название: Machine Learning For Network Traffic and Video Quality Analysis: Develop and Deploy Applications Using jаvascript and Node.js
Автор: Tulsi Pawan Fowdur, Lavesh Babooram
Издательство: Apress
Год: 2024
Страниц: 475
Язык: английский
Формат: pdf
Размер: 15.1 MB

This book offers both theoretical insights and hands-on experience in understanding and building machine learning-based Network Traffic Monitoring and Analysis (NTMA) and Video Quality Assessment (VQA) applications using jаvascript. jаvascript provides the flexibility to deploy these applications across various devices and web browsers. The book begins by delving into NTMA, explaining fundamental concepts and providing an overview of existing applications and research within this domain. It also goes into the essentials of VQA and offers a survey of the latest developments in VQA algorithms. The book includes a thorough examination of machine learning algorithms that find application in both NTMA and VQA, with a specific emphasis on classification and prediction algorithms such as the Multi-Layer Perceptron and Support Vector Machine. The book also explores the software architecture of the NTMA client-server application. This architecture is meticulously developed using HTML, CSS, Node.js, and jаvascript. Practical aspects of developing the Video Quality Assessment (VQA) model using jаvascript and Java are presented. Lastly, the book provides detailed guidance on implementing a complete system model that seamlessly merges NTMA and VQA into a unified web application, all built upon a client-server paradigm. By the end of the book, you will understand NTMA and VQA concepts and will be able to apply machine learning to both domains and develop and deploy your own NTMA and VQA applications using jаvascript and Node.js.

Название: Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling: Second Edition
Автор: Måns Thulin
Издательство: CRC Press
Год: 2025
Страниц: 493
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. R is not like other statistical software packages. It is free, versatile, fast, and modern. It has a large and friendly community of users that help answer questions and develop new R tools. With more than 17,000 add-on packages available, R offers more functions for data analysis than any other statistical software. This includes specialised tools for disciplines as varied as political science, environmental chemistry, and astronomy, and new methods come to R long before they come to other programs. R makes it easy to construct reproducible analyses and workflows that allow you to easily repeat the same analysis more than once. R is not like other programming languages. It was developed by statisticians as a tool for data analysis and not by software engineers as a tool for other programming tasks. Some books on R focus entirely on Data Science – data wrangling and exploratory data analysis – ignoring the many great tools R has to offer for deeper data analyses. Many introductory books on statistical methods put too little focus on recent advances in computational statistics and advocate methods that have become obsolete. Far too few books contain discussions of ethical issues in statistical practice. This book aims to cover all of these topics and show you the state-of-the-art tools for all these tasks. It covers data science and (modern!) classical statistics as well as predictive modelling and machine learning, and deals with important topics that rarely appear in other introductory texts, such as simulation. It is written for R 4.3 or later and will teach you powerful add-on packages like data.table, dplyr, ggplot2, and caret. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices.

Название: 97 Things Every Application Security Professional Should Know: Collective Wisdom from the Experts
Автор: Reet Kaur, Yabing Wang
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 402
Язык: английский
Формат: epub
Размер: 34.2 MB

In this fast-advancing technology world, almost everything is written as software or application. Cybersecurity, or information security, has always been a very broad and comprehensive field and has been a fast-evolving area for the past 10–20 years. Within, there are many domains, such as risk management, security operations, network and infrastructure security, identity access management, and others. This book focuses on one particular domain called application security (AppSec). That’s because, in today’s modern world, software development has become the core of any product or service. As such, ensuring the security of any product or application development is critical to the success of your business. This book is a collection of wisdom from 77 security experts in application security across various industries. Organized into 12 topics, the book covers web applications, mobile applications, APIs, and the Internet of Things (IoT) (embedded systems). It also expands the safeguards to both on-prem and in-cloud development. More importantly, it explains all angles of AppSec such as secure software development life cycle (SDLC) practice, threat modeling, code scanning and testing, vulnerability management, and how to run a successful application security program. The book also provides insight into two emerging topics: software supply chain security and AI security. It is a treasure trove of those security practitioners’ practical advice, distilled into bite-sized essays for both beginners and seasoned professionals in application security and cybersecurity.

Название: The Python Book - 18th Edition, 2024
Автор: Jon White (Editor)
Издательство: Future Publishing
Год: 2024
Страниц: 180
Язык: английский
Формат: pdf
Размер: 90.9 MB

In this revised edition of The Python Book, you'll find plenty of creative projects to help you get to grips with one of the fastest-growing programming languages around. Its powerful functionality works brilliantly with the Raspberry Pi, but you'll also find plenty of tutorials that focus on Python's effectiveness away from the Pi. You'll learn how to code with Python from the very beginning with our comprehensive masterclass, then go on to complete tutorials to consolidate your skills and become fluent in the language. Become a true Python expert with the wealth of information contained in this bookazine.

Название: Осваиваем архитектуру Transformer: Разработка современных моделей с помощью передовых методов обработки естественного языка
Автор: Йылдырым C., Асгари-Ченаглу М.
Издательство: ДMK
Год: 2022
Cтраниц: 320
Формат: pdf
Размер: 18 мб
Язык: русский

В этой книге рассказывается, как создавать различные приложения NLP на основе трансформеров, используя библиотеку Python Transformers. Вы познакомитесь с архитектурой трансформеров и напишете свою первую программу для работы с моделями на основе этой передовой технологии.

Название: The Complete Python Coding Manual - 22th Edition, 2024
Автор: Papercut Limited
Издательство: Papercut Limited
Год: 2024
Страниц: 164
Язык: английский
Формат: pdf
Размер: 84.5 MB

Существует несколько успешных языков программирования, подобных Python, и благодаря его уникальной разработке любой желающий может научиться кодировать технические проекты, такие как Большой адронный коллайдер, получение первого набора данных размером в петабайт с изображениями черных дыр и создание искусственного интеллекта следующего поколения. Освоить Python может любой желающий: освоение не займет много времени, но начинать нужно с малого. Это руководство поможет вам заложить основу для будущего программирования на Python, начиная с установки языка на ваш компьютер и заканчивая взаимодействием с пользователем и созданием сложных переменных. На этих страницах вы можете найти все, что вам нужно, чтобы стать программистом на Python и перейти на новый уровень. Если вы хотите улучшить свою карьеру, изучая Python, или просто хотите весело провести время и открыть для себя что-то новое, наши пошаговые руководства и обучающие программы предоставят вам необходимые знания. Начните программировать на Python!

Название: Ethical Artificial Intelligence in Power Electronics
Автор: Tarandeep Kaur Bhatia, Salma El Hajjami, Keshav Kaushik, Gayo Diallo
Издательство: CRC Press
Год: 2024
Страниц: 177
Язык: английский
Формат: pdf (true)
Размер: 24.8 MB

This book focuses on the techniques of Artificial Intelligence that are mainly used in the power electronics field for the optimization of lost vehicle power. With the intention of optimizing the powerful energy of the vehicles and producing reliable energy, the most efficient methods, algorithms, and strategies of Ethical Artificial Intelligence (AI) are being applied. By employing machine learning methods, the optimization of power energy in vehicles can be quickly recovered and managed efficiently. In today’s bustling world, power energy is indispensable for progress, yet in congested Vehicular Ad-hoc Networks (VANETs), vehicles often face power depletion and decreased efficiency. This book explores these challenges, encompassing not only power but also other critical power electronics within vehicles. We aim to introduce innovative approaches, leveraging ethical AI methods, to optimize energy performance in the face of these difficulties. Through this exploration, we seek to provide practical insights into navigating congested VANET environments while upholding ethical principles in technological advancements. Our book will discuss the current power energy concerns faced by vehicles and also contribute a novel strategy to overcome those concerns. The employment of ethical AI in vehicular power energy will undoubtedly improve the effectiveness and production of vehicles.

Название: Hypermodern Python Tooling: Building Reliable Workflows for an Evolving Python Ecosystem (Final Release)
Автор: Claudio Jolowicz
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 541
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Keeping up with the Python ecosystem can be daunting. Its developer tooling doesn't provide the out-of-the-box experience native to languages like Rust and Go. When it comes to long-term project maintenance or collaborating with others, every Python project faces the same problem: how to build reliable workflows beyond local development while staying in sync with the evolving ecosystem. With this hands-on guide, Python developers will learn how to forge the moving parts of a Python project into an easy-to-use toolchain, using state-of-the-art tools including Poetry, Nox, pytest, mypy, pre-commit, Black, Ruff, uv, Rye, Hatch, and more. Author Claudio Jolowicz shows you how to create robust Python project structures complete with unit tests, static analysis, code formatting, and type checking. You don’t strictly need these tools to write Python software. Fire up your system’s Python interpreter and get an interactive prompt. Save your Python code as a script for later. Why use anything beyond an editor and a shell? This is not a rhetorical question. Every tool you add to your development workflow should have a clear purpose and bring benefits that outweigh the costs of using it. Generally, the benefits of development tooling become manifest when you need to make development sustainable over time. At some point, publishing your module on the Python Package Index will be easier than emailing it to your users. This book assumes that you have a basic knowledge of the Python programming language. The only tooling you need to be familiar with are the Python interpreter, an editor or IDE, and the command line of your operating system.

Название: Artificial Intelligence and Machine Learning in Drug Design and Development
Автор: Abhirup Khanna, May El Barachi, Sapna Jain, Manoj Kumar
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 670
Язык: английский
Формат: pdf (true)
Размер: 88.5 MB

The book is a comprehensive guide that explores the use of Artificial Intelligence (AI) and Machine Learning (ML) in drug discovery and development covering a range of topics, including the use of molecular modeling, docking, identifying targets, selecting compounds, and optimizing drugs. The intersection of Artificial Intelligence and Machine Learning within the field of drug design and development represents a pivotal moment in the history of healthcare and pharmaceuticals. The remarkable synergy between cutting-edge technology and the life sciences has ushered in a new era of possibilities, offering unprecedented opportunities, formidable challenges, and a tantalizing glimpse into the future of medicine. AI can be applied to all the key areas of the pharmaceutical industry, such as drug discovery and development, drug repurposing, and improving productivity within a short period. Contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Moreover, AI helps in predicting the efficacy and safety of molecules and gives researchers a much broader chemical pallet for the selection of the best molecules for drug testing and delivery. In this context, drug repurposing is another important topic where AI can have a substantial impact. With the vast amount of clinical and pharmaceutical data available to date, AI algorithms find suitable drugs that can be repurposed for alternative use in medicine. This book is a comprehensive exploration of this dynamic and rapidly evolving field. In an era where precision and efficiency are paramount in drug discovery, AI and ML have emerged as transformative tools, reshaping the way we identify, design, and develop pharmaceuticals.

Название: Explainable Artificial Intelligence in Healthcare Systems
Автор: A. Anitha Kamaraj, Debi Prasanna Acharjya
Издательство: Nova Science Publishers
Серия: Computer Science, Technology and Applications
Год: 2024
Страниц: 389
Язык: английский
Формат: pdf (true)
Размер: 37.7 MB

The twenty chapters in this book offer a comprehensive overview of the theory, algorithms, and applications of interpretable and Explainable Artificial Intelligence (XAI). They cover recent advances in the field of healthcare, reflect the current discourse, and offer suggestions for future research. The book is divided into four sections: primitive concepts of XAI, XAI in smart tele-medicine and tele-health, public health application using XAI, and medical imaging classification using XAI. Thus, the book covers a comprehensive set of material ranging from fundamentals to image analysis employing XAI ideas. Explainable Artificial Intelligence (XAI) is a next-generation research field that aims to make AI and deep models more human-interpretable without compromising performance. Artificial Intelligence (AI), Machine Learning, and Deep Learning models have paved a solid predictive path in numerous real-life applications, including medical imaging and healthcare tasks. In healthcare, decisions and investigation process is taken with great care, but risks are associated with diagnosing a disease. The diagnosis of the patient's illness is analyzed based on various symptoms, observations, test reports, and experience. However, there is a chance of uncertainty while analyzing the data. Additionally, a second opinion is sought in many cases. Using AI, it is possible to automate the model to mimic the process of a domain expert so that a physician can think of alternative decisions while making decisions. Specifically, XAI provides a clear picture of prediction concerning Deep Learning models and the use of predictedresults in healthcare applications. This book aims to provide the basic and intermediate notions of XAI and its application to healthcare applications.