Litgu.ru - Литературный Гуру

Название: Git & GitHub Visual Guide: The Hands-On Manual for Complete Beginners to Master Version Control and Remote Coding Collaboration
Автор: Ben Bloomfield, David Ocean, Atlas Skylark, Valeria Celis
Издательство: D-Libro
Год: 2024
Страниц: 515
Язык: английский
Формат: epub
Размер: 27.5 MB

Begin your journey into the world of Git and GitHub with this comprehensive hands-on manual designed for complete beginners. This book demystifies the complexities of version control and remote collaboration using Git and GitHub, breaking them down into accessible, step-by-step chapters that ensure you grasp the fundamental concepts needed to manage your coding projects efficiently. Version Control System (VCS) is one of the most critical tools for coding and digital application development. Git is the most commonly used version control system, created by Linus Torvalds in 2005 for the development of the Linux kernel. GitHub is one of the most popular Remote Repository services based on the Git platform. In this course, we'll explain how to use Git as a version control system and how to collaborate with other developers on GitHub. Git and GitHub are often explained together, however, they are different things. Git is a version control system while GitHub is a repository service provided by GitHub, Inc. Typically, remote repository services also offer task management features besides collaborative coding features. For example, GitHub provides Git Issues that allow you to input tasks and share them with your team. GitHub or other remote repository services can also integrate with other services for workflow automation. This guide not only introduces you to the essentials of Git and GitHub but also equips you with the knowledge to effectively plan, implement, and manage your software development projects.

Название: Face De-identification: Safeguarding Identities in the Digital Era
Автор: Yunqian Wen, Bo Liu, Li Song, Jingyi Cao, Rong Xie
Издательство: Springer
Год: 2024
Страниц: 195
Язык: английский
Формат: pdf (true), epub
Размер: 23.9 MB

This book provides state-of-the-art Face De-Identification techniques and privacy protection methods, while highlighting the challenges faced in safeguarding personal information. It presents three innovative image privacy protection approaches, including differential private k-anonymity, identity differential privacy guarantee and personalized and invertible Face De-Identification. In addition, the authors propose a novel architecture for reversible Face Video De-Identification, which utilizes deep motion flow to ensure seamless privacy protection across video frames. This book is a compelling exploration of the rapidly evolving field of Face De-Identification and privacy protection in the age of advanced AI-based face recognition technology and pervasive surveillance. This insightful book embarks readers on a journey through the intricate landscape of facial recognition, Artificial Intelligence (AI), social network and the challenges posed by the digital footprint left behind by individuals in their daily lives. The authors also explore emerging trends in privacy protection and discuss future research directions. Face recognition is a biometric technology that automatically recognizes people’s facial features including statistics and geometric features, which is one of the most important applications of image analysis and understanding. Researchers working in Computer Science, Artificial Intelligence, Machine Learning, data privacy and cybersecurity as well as advanced-level students majoring in Computers Science will find this book useful as reference or secondary text. Professionals working in the fields of biometrics, data security, software development and facial recognition technology as well as policymakers and government officials will also want to purchase this book.

Название: Integrating Artificial and Human Intelligence through Agent Oriented Systems Design
Автор: Michael E. Miller, Christina F. Rusnock
Издательство: CRC Press
Год: 2025
Страниц: 322
Язык: английский
Формат: pdf (true)
Размер: 13.1 MB

This book departs from the assumption that Artificial Intelligence (AI) systems will provide a maximum advantage by replacing human cognitive processing. Instead, this book subscribes to the assumption that AI systems will provide a maximal advantage when the system is specifically designed to augment human intelligence. It provides methods for designing effective systems that include one or more humans and one or more AI entities and uses the approach that assumes automation does not replace human activity but fundamentally changes the structure of human work when AI is added to existing systems. Integrating Artificial and Human Intelligence through Agent Oriented Systems Design discusses the potential impact of AI on human work and life and explores why teamwork is necessary today for complex work environments. The book explains the processes and methods humans employ to effectively team with one another and presents the elements of artificial agents that permit them to function as team members in joint human and artificial teams. It discusses design goals and illustrates how the methods that have been used to model the complex interactions among human and artificial agents can be expanded to enable the design of interaction between them to make possible the attainment of the shared goals. Model-Based Systems Engineering (MBSE) tools that provide logical designs of human–agent teams, the AI within these teams, training to be deployed for human and artificial agent team members, and the interfaces between human and artificial agent team members are all covered. MBSE files containing profiles and examples for building MBSE models used in the design approach are featured on the author’s website. When applying this text in computer science courses, faculty may consider exposing the students to the Unified Markup Language and possibly Systems Modeling Language prior to a course that applies this text or deliberately incorporate external sources providing an introduction to these languages as part of this course. This book is an ideal read for students, professors, engineers, and project managers associated with designing and developing AI systems or systems that seek to incorporate AI.

Название: Mastering Matplotlib with Python for Developers: Effective techniques for data visualization with Python
Автор: Hiram Esteban Jimenez, Jenna G Melendez
Издательство: Independently published
Год: 2024
Страниц: 388
Язык: английский
Формат: epub
Размер: 10.1 MB

Python also has a large and supportive community with many online resources and documentation. Many tutorials, forums, and videos can help beginners get started with Python programming. Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002. One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram etc.

Название: Practical Rust 1.x Cookbook, Second Edition: 100+ Solutions for beginners to practice rust programming across CI/CD, kubernetes, networking, code performance and microservices
Автор: Lloyd Frank
Издательство: GitforGits
Год: 2024
Страниц: 439
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

If you want to learn how to program in Rust and create strong, reliable apps with the most recent version Rust 1.68, then you need the "Practical Rust 1.x Cookbook (Second Edition)" - a book full of real-world solutions. An extensive collection of practical recipes covering a wide range of topics, this edition expands upon the first and guarantees that you will be well-versed in the complexities of Rust. You will learn the fundamentals of programming, how to set up a development environment, and the syntax of Rust in the first few chapters. Data structures, pattern matching, error handling, and concurrency are all covered in the recipes, which will set you up for success with more complex topics. As you go through the book, you'll learn how to write efficient and secure code by managing memory, using Rust's ownership model, and borrowing rules. You will gain an understanding of how to make use of robust Rust features such as traits, generics, and lifetimes to build code that is both flexible and reusable. The testing and debugging chapters provide the necessary tools and techniques to guarantee that your applications are dependable and free of bugs. By learning about SIMD instructions, compiler optimization flags, and parallel processing with libraries like rayon, you can greatly improve the efficiency of your code. In the more advanced chapters, you will learn how to use static and dynamic analysis, fuzz testing, and external high-performance libraries to make your applications even better. In no time at all, you will be able to build robust, high-performance Rust applications by following these potent recipes and becoming an expert Rust programmer.

Название: Ultimate Machine Learning with ML.NET: Build, Optimize, and Deploy Powerful Machine Learning Models for Data-Driven Insights with ML.NET, Azure Functions, and Web API
Автор: Kalicharan Mahasivabhattu, Deepti Bandi
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 247
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

“Empower Your .NET Journey with Machine Learning”. Dive into the world of Machine Learning for data-driven insights and seamless integration in .NET applications with the Ultimate Machine Learning with ML.NET. The book begins with foundations of ML.NET and seamlessly transitions into practical guidance on installing and configuring it using essential tools like Model Builder and the command-line interface. Next, it dives into the heart of Machine Learning tasks using ML.NET, exploring classification, regression, and clustering with its versatile functionalities. It will delve deep into the process of selecting and fine-tuning algorithms to achieve optimal performance and accuracy. You will gain valuable insights into inspecting and interpreting ML.NET models, ensuring they meet your expectations and deliver reliable results. It will teach you efficient methods for saving, loading, and sharing your models across projects, facilitating seamless collaboration and reuse. ML.NET is particularly distinctive for its deep integration with the .NET ecosystem, enabling developers to leverage their existing expertise in C# and other .NET languages to build and deploy Machine Learning models seamlessly. Whether you are a seasoned software engineer or a budding data scientist, ML.NET opens the door to a world of opportunities in Machine Learning, providing a platform where creativity and problem-solving converge. ML.NET is known for its versatility, offering support for a wide range of Machine Learning tasks, including classification, regression, clustering, and recommendation systems. Its open-source nature, platform independence, and comprehensive documentation make it a preferred choice for many practitioners who seek a framework that is both powerful and easy to learn. Whether you are working on web applications, mobile apps, or desktop software, ML.NET is designed to be a part of your toolkit, making Machine Learning accessible and attainable for everyone.

Название: The Modern Business Data Analyst: A Case Study Introduction into Business Data Analytics with CRISP-DM and R
Автор: Dominik Jung
Издательство: Springer
Год: 2024
Страниц: 312
Язык: английский
Формат: pdf (true)
Размер: 13.6 MB

This book illustrates and explains the key concepts of business data analytics from scratch, tackling the day-to-day challenges of a business data analyst. It provides you with all the professional tools you need to predict online shop sales, to conduct A/B tests on marketing campaigns, to generate automated reports with PowerPoint, to extract datasets from Wikipedia, and to create interactive analytics Web apps. Alongside these practical projects, this book provides hands-on coding exercises, case studies, the essential programming tools and the CRISP-DM framework which you'll need to kickstart your career in business data analytics. The different chapters prioritize practical understanding over mathematical theory, using realistic business data and challenges of the Junglivet Whisky Company to intuitively grasp key concepts and ideas. Designed for beginners and intermediates, this book guides you from business data analytics fundamentals to advanced techniques, covering a large number of different techniques and best-practices which you can immediately exploit in your daily work. The book does not assume that you have an academic degree or any experience with business data analytics or Data Science. All you need is an open mind, willingness to puzzle and think mathematically, and the willingness to write some R code. This book is your all-in-one resource to become proficient in business data analytics with R, equipped with practical skills for the real world.

Название: Artificial Intelligence in Forensic Science: An Emerging Technology in Criminal Investigation Systems
Автор: Kavita Saini, Swaroop S. Sonone, Mahipal Singh Sankhla, Naveen Kumar
Издательство: CRC Press
Год: 2024
Страниц: 206
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Artificial Intelligence in Forensic Scienceaddresses the current and emerging opportunities being utilized to apply modern Artificial Intelligence (AI) technologies to current forensic and investigation practices. The book also showcases the increasing benefits of AI where and when it can be applied to various techniques and forensic disciplines. The increasing rate of sophisticated crimes has increased the opportunity and need for the forensic field to explore a variety of emerging technologies to counter criminals—and artificial intelligence is no exception. There are many current investigative challenges that, with ingenuity and application, can be helped with the application of AI, especially in the digital forensic and cyber-crime arena. The book also explains many practical studies that have been carried out to test AI technologies in crime detection, uncovering evidence, and identifying perpetrators. In the last decade, the use of AI is common in many fields and now is an ideal time to look at the various ways AI can be integrated into judicial, forensic, and criminal cases to better collect and analyze evidence, thereby improving outcomes. Artificial neural networks (ANN), support vector machines (SVM), and genetic algorithms (GA) are examples of Machine Learning approaches that play a significant role in providing unconventional solutions for fingerprint identification challenges. Making a feature vector and tutoring (teaching) the computer how to process it in accordance with predetermined criteria are the objectives of these techniques.

Название: Algorithmic Trading Systems and Strategies: A New Approach: Design, Build, and Maintain an Effective Strategy Search Mechanism
Автор: Viktoria Dolzhenko
Издательство: Apress
Год: 2024
Страниц: 452
Язык: английский
Формат: pdf (true), epub (true)
Размер: 12.1 MB

Design and develop a complex trading system from idea to operation. Old approaches were based on manually searching for strategy ideas. This book shows you how to create a system that will generate, optimize, and launch profitable strategies into a fully automatic mode. Imagine a system that only requires access to the Internet and electricity from a trader. This book describes the architecture and features of such a system and provides recommendations for further development. Most books cover only the implementation stage and overlook the design and maintenance of these systems. Here, you’ll walk through the entire process of creating a complex, scalable and easily supported system. For example, you’ll design an application based on microservice architecture and learn about development environments. You’ll also examine the advantages of horizontal scaling in the context of creating trading systems. Along the way, you’ll set up Kubernetes, connect the monitoring system, and understand the intricacies of continuous integration and continuous delivery. Testing and identifying only dozens of strategies is a thing of the past. Algorithmic Trading Systems and Strategies: A New Approach shows you how to analyze thousands in the same amount of time. The global trend to automate and algorithmize everything has now come to trading, and every year the number of algorithmic systems that help traders or that even trade independently is growing. Anyone can buy an algorithmic robot; you don’t need to be a programmer. Of course, you can create your own trading system using your own ideas. But what do you do if you have no programming skills? Genetic algorithms are a popular type of algorithm belonging to the population class. These are the ones I will use in the search for the optimal strategy. This class of algorithms was based on the ideas of Darwin’s theory of natural selection.

Название: Beginning Mathematica and Wolfram for Data Science: Applications in Data Analysis, Machine Learning, and Neural Networks, 2nd Edition
Автор: Jalil Villalobos Alva
Издательство: Apress
Год: 2024
Страниц: 476
Язык: английский
Формат: pdf
Размер: 23.6 MB

Enhance your Data Science programming and analysis with the Wolfram programming language and Mathematica, an applied mathematical tools suite. This second edition introduces the latest LLM Wolfram capabilities, delves into the exploration of data types in Mathematica, covers key programming concepts, and includes code performance and debugging techniques for code optimization. You’ll gain a deeper understanding of data science from a theoretical and practical perspective using Mathematica and the Wolfram Language. Learning this language makes your data science code better because it is very intuitive and comes with pre-existing functions that can provide a welcoming experience for those who use other programming languages. Existing topics have been reorganized for better context and to accommodate the introduction of Notebook styles. The book also incorporates new functionalities in code versions 13 and 14 for imported and exported data. You’ll see how to use Mathematica, where data management and mathematical computations are needed. Along the way, you’ll appreciate how Mathematica provides an entirely integrated platform: its symbolic and numerical calculation result in a mized syntax, allowing it to carry out various processes without superfluous lines of code. You’ll learn to use its notebooks as a standard format, which also serves to create detailed reports of the processes carried out. For data scientists who are new to using Wolfram and Mathematica as a programming language or tool. Programmers should have some prior programming experience, but can be new to the Wolfram language.