Название: Getting (more out of) Graphics: Practice and Principles of Data Visualisation
Автор: Antony Unwin
Издательство: CRC Press
Серия: Data Science Series
Год: 2024
Страниц: 447
Язык: английский
Формат: pdf (true), epub (true)
Размер: 62.7 MB

Data graphics are used extensively to present information. Understanding graphics is a lot about understanding the data represented by the graphics, having a feel not just for the numbers themselves, the reliability and uncertainty associated with them, but also for what they mean. This book presents a practical approach to data visualisation with real applications front and centre. The first part of the book is a series of case studies, each describing a graphical analysis of a real dataset. The second part pulls together ideas from the case studies and provides an overview of the main factors affecting understanding graphics. There is a range of different software systems for drawing graphics and everyone has to decide for themselves which one(s) they want to use. Getting details right may be easy with one software and difficult with another. New software releases may provide new options and sometimes substantial improvements. How exactly the graphics are drawn is not important, what the graphics look like and whether they achieve the aims they are intended to is. This book is primarily about how to interpret graphics, not so much about how to draw them. The graphics in this book have all been drawn with R. Other software could be used to draw the same or similar graphics; in some cases, it might be easier, in others harder. Use the software that suits you best. The R code for the book can be found in the gitbook version on the web. You will need to have some knowledge and experience of working with R (R Core Team) and the group of R packages known as the Tidyverse. Some users prefer base R for drawing their graphics and may use packages such as lattice and vcd.

Название: DevOps for Software Engineers
Автор: Srinikhita Kothapalli, Pavan Kumar Gade, Hari Priya Kommineni, Aditya Manikyala
Издательство: Warta Say
Год: 2024
Страниц: 184
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

The evolution of software development has transformed dramatically over the past decade, driven by the need for faster, more reliable delivery of applications and services. In this rapidly changing landscape, DevOps has emerged as a crucial methodology, bridging the gap between development and operations. It integrates practices and tools that foster collaboration, automate processes, and enhance the efficiency of software delivery. "DevOps for Software Engineers" is born out of our collective experience and passion for improving the way software is developed, deployed, and maintained in the modern era. This book is designed to serve as a comprehensive guide for software engineers who are either new to DevOps or looking to deepen their understanding of the subject. We have structured the content to cover the foundational concepts of DevOps, practical implementations, and advanced techniques that can be applied in real-world scenarios. The book encompasses a broad range of topics, including continuous integration and continuous delivery (CI/CD), infrastructure as code, automated testing, monitoring, and security, among others. Each chapter is crafted to build on the previous one, creating a cohesive learning experience that progresses from basic principles to more complex implementations. Our goal is to equip software engineers with the knowledge and tools necessary to thrive in a DevOps-driven environment. We believe that by mastering these concepts, engineers can significantly contribute to the efficiency, reliability, and scalability of software systems. Furthermore, this book aims to demonstrate how DevOps practices can be seamlessly integrated into existing workflows, ultimately leading to more robust and resilient software. Whether you are beginning your journey into DevOps or seeking to enhance your expertise, we trust that you will find the content both informative and inspiring.

Название: Tech Leadership Playbook: Building and Sustaining High-Impact Technology Teams
Автор: Alexsandro Souza
Издательство: Apress
Год: 2024
Страниц: 233
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Immerse yourself in this indispensable resource for leaders tasked with the challenge of building or managing effective software development teams. This book is based on practical wisdom, offering actionable guidance to foster high-performing teams that excel in their projects. Despite the pivotal role leadership plays in a team's success, there aren't many companies that employ structured, best-practice-driven leadership methods. The core of the book covers several critical areas essential for any tech leader's success: building high-performance teams, project management, code quality, software design and architecture, software development life cycle (SDLC), software quality insurance, observability, technology and business alignment. The relevance of structured, principled leadership in tech has never been more important. Tech Leadership Playbook aims to equip leaders with the knowledge and tools necessary to navigate the challenges of evolving business successfully. For senior engineers, tech leaders, engineering managers, CTO, CIO, project managers, agile coaches, and founders.

Название: Python Data Analysis: Transforming Raw Data into Actionable Intelligence with Python's Data Analysis Capabilities
Автор: Tom Lesley
Издательство: May Reads
Год: 2024
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

Python has emerged as a powerful language for data analysis, thanks to its extensive libraries and ease of use. Python for Data Analysis is a comprehensive guide that will help beginners and experienced professionals learn how to use Python for data analysis. This book covers everything from the basics of Python programming to advanced topics like Machine Learning, Deep Learning, and Bayesian data analysis. The book begins by introducing readers to the basics of Python programming and the key data structures used in data analysis. It then covers the various data preparation and exploratory data analysis techniques that are commonly used in the field. The book also covers advanced topics like Machine Learning, where readers will learn about regression, classification, clustering, and dimensionality reduction techniques. The book also includes a chapter on natural language processing (NLP), where readers will learn about text classification, sentiment analysis, and topic modeling. In addition, the book covers big data analytics, where readers will learn how to use distributed computing frameworks like PySpark and Dask to handle large datasets. The book also covers cloud-based platforms like AWS and Google Cloud, where readers will learn how to scale their Python code to handle big data analysis tasks. The book concludes with a chapter on advanced topics like Deep Learning, reinforcement learning, and Bayesian data analysis. Readers will also learn about advanced visualization techniques that can help them present their findings in a clear and concise manner. Abundance of libraries Python has a broad range of libraries designed to handle data analysis tasks, such as data cleaning, data visualization, Machine Learning, and statistics. Some of the most popular libraries for data analysis include NumPy, Pandas, Matplotlib, Seaborn, SciPy, and Scikit-Learn.

Название: Mathematical Engineering of Deep Learning
Автор: Benoit Liquet, Sarat Moka, Yoni Nazarathy
Издательство: CRC Press
Серия: Data Science Series
Год: 2025
Страниц: 415
Язык: английский
Формат: pdf (true), epub
Размер: 39.8 MB

Mathematical Engineering of Deep Learning provides a complete and concise overview of Deep Learning using the language of mathematics. The book provides a self-contained background on Machine Learning and optimization algorithms and progresses through the key ideas of Deep Learning. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art Artificial Intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of Deep Learning.

Название: The Computational Content Analyst: Using Machine Learning to Classify Media Messages
Автор: Chris J. Vargo
Издательство: Routledge
Год: 2025
Страниц: 144
Язык: английский
Формат: pdf (true), epub, mobi
Размер: 10.1 MB

Most digital content, whether it be thousands of news articles or millions of social media posts, is too large for the naked eye alone. Often, the advent of immense datasets requires a more productive approach to labeling media beyond a team of researchers. This book offers practical guidance and Python code to traverse the vast expanses of data—significantly enhancing productivity without compromising scholarly integrity. We’ll survey a wide array of computer-based classification approaches, focusing on easy-to-understand methodological explanations and best practices to ensure that your data is being labeled accurately and precisely. By reading this book, you should leave with an understanding of how to select the best computational content analysis methodology to your needs for the data and problem you have. This guide gives researchers the tools they need to amplify their analytical reach through the integration of content analysis with computational classification approaches, including Machine Learning and the latest advancements in Generative Artificial Intelligence (AI) and Large Language Models (LLMs). It is particularly useful for academic researchers looking to classify media data and advanced scholars in mass communications research, media studies, digital communication, political communication, and journalism. Complementing the book are online resources: datasets for practice, Python code scripts, extended exercise solutions, and practice quizzes for students, as well as test banks and essay prompts for instructors. From here we will assume that you have a fundamental working knowledge of manipulating data in Python. If you are not a coder, we’ll discuss emerging generative AI tools that can help you code.

Название: Convergence of Blockchain and Explainable Artificial Intelligence: BlockXAI
Автор: Akansha Singh, Krishna Kant Singh
Издательство: River Publishers
Год: 2024
Страниц: 180
Язык: английский
Формат: pdf (true), epub (true)
Размер: 20.4 MB

Explainable AI (XAI) is an upcoming research field in the domain of machine learning. This book aims to provide a detailed description of the topics related to XAI and Blockchain. These two technologies can benefit each other, and the research outcomes will benefit society in multiple ways. Existing AI systems make decisions in a black box manner. Explainable AI delineates how an AI system arrived at a particular decision. It inspects the steps and models that are responsible for making a particular decision. It is an upcoming trend that aims at providing explanations to the AI decisions. Blockchain is emerging as an effective technique for XAI. It enables accessibility to digital ledgers amongst the various AI agents. The AI agents collaborate using consensus and decisions are saved on Blocks. These blocks can be traced back but cannot be changed. Thus, the combination of AI with blockchain provides transparency and visibility to all AI decisions. BlockXAI is also being widely used for improving data security and intelligence. The decisions made are consensus based and decentralized leading to highly efficient AI systems. This book also covers topics that present the convergence of Blockchain with explainable AI and will provide researchers, academics, and industry experts with a complete guide to BlockXAI. This book provides a detailed description of the topics related to XAI and Blockchain.

Название: PowerShell for Cybersecurity: Scripting Defense and Offense
Автор: Laszlo Bocso
Издательство: Independently published
Год: 2024
Страниц: 631
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

"PowerShell for Cybersecurity: Scripting Defense and Offense" was born out of a need to bridge the gap between traditional IT administration and cybersecurity practices. This book is designed to provide readers with a comprehensive understanding of how PowerShell can be leveraged to enhance security operations, automate defensive measures, and execute sophisticated offensive strategies. Whether you're a security analyst, system administrator, red teamer, or ethical hacker, this book will equip you with the knowledge and tools necessary to harness the full potential of PowerShell in your cybersecurity endeavors. PowerShell has become a ubiquitous tool in both enterprise and security environments due to its powerful scripting capabilities and extensive access to system resources. Unlike other scripting languages, PowerShell is deeply embedded in the Windows operating system, providing a native and efficient way to interact with the underlying system components. This deep integration makes PowerShell an invaluable asset for automating repetitive tasks, managing large-scale environments, and implementing security policies. By the end of this book, readers will have a comprehensive understanding of how to leverage PowerShell for both defensive and offensive cybersecurity operations. They will be equipped with the skills to automate security tasks, detect and respond to threats, conduct penetration tests, and develop custom security solutions using PowerShell. Whether you're a seasoned cybersecurity professional or just starting your journey in the field, "PowerShell for Cybersecurity: Scripting Defense and Offense" provides the knowledge and practical skills needed to excel in today's dynamic cybersecurity landscape. With its balanced approach to both defensive and offensive techniques, this book is an indispensable resource for anyone looking to master PowerShell for cybersecurity purposes.

Название: Machine Learning Hybridization and Optimization for Intelligent Applications
Автор: Tanvir Habib Sardar, Bishwajeet Kumar Pandey
Издательство: CRC Press
Серия: Computational Intelligence Techniques
Год: 2025
Страниц: 367
Язык: английский
Формат: pdf (true), epub
Размер: 33.6 MB

This book discusses state-of-the-art reviews of the existing Machine Learning techniques and algorithms including hybridizations and optimizations. It covers applications of Machine Learning via Artificial Intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, pattern recognition approaches to functional magnetic resonance imaging, image and speech recognition, automatic language translation, medical diagnostic, stock market prediction, traffic prediction, and product automation. In recent years, the fusion of Machine Learning methodologies with optimization techniques has significantly advanced the realm of intelligent applications across diverse domains. This book delves into the symbiotic relationship between Machine Learning hybridization and optimization, presenting a comprehensive exploration of their integration to empower intelligent systems. The amalgamation of Machine Learning and optimization has revolutionized the way we approach problem-solving, enabling us to create sophisticated applications that adapt, evolve, and enhance their performance over time. This synergy has paved the way for innovation in various fields, from healthcare and finance to manufacturing and beyond. This book is aimed at graduate students and researchers in Machine Learning, Artificial Intelligence, and electrical engineering.

Название: System Reliability and Security: Techniques and Methodologies
Автор: Javaid Iqbal, Faheem Syeed Masoodi, Ishfaq Ahmad Malik, Shozab Khurshid, Iqra Saraf
Издательство: CRC Press
Год: 2024
Страниц: 273
Язык: английский
Формат: pdf (true), epub (true)
Размер: 29.6 MB

Because of the growing reliance on software, concerns are growing as to how reliable a system is before it is commissioned for use, how high the level of reliability is in the system, and how many vulnerabilities exist in the system before its operationalization. Equally pressing issues include how to secure the system from internal and external security threats that may exist in the face of resident vulnerabilities. These two problems are considered increasingly important because they necessitate the development of tools and techniques capable of analyzing dependability and security aspects of a system. These concerns become more pronounced in the cases of safety-critical and mission-critical systems. System Reliability and Security: Techniques and Methodologies focuses on the use of soft computing techniques and analytical techniques in the modeling and analysis of dependable and secure systems. Software Testing and Quality Assurance: In general, testing is intended to uncover the errors that creep in unintentionally during the design and construction of software systems/models or to verify whether the model meets the expected objectives. In contrast to traditional software development, AI/ML models are constructed inductively. That is, the behavior of the model is generated/designed from training data. Therefore, besides noisy data, the potential causes for undesired behavior in AI/ML include execution environment, design mis-specification, poor choice of model, program code, numerical instability, and non-convex objectives.