Название: Java Systems Design Interview Challenger: Pass Java System Design interviews by understanding the fundamentals of a cloud system’s components. Don’t try to learn all the tools, focus on concepts instead
Автор: Rafael Chinelato del Nero
Издательство: Leanpub
Год: 2024-05-01
Страниц: 290
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

What about learning about System Design with a Java flavor? In this book, you will learn essential fundamentals for System design within the Java ecosystem so you know what technologies to use to design a Java system! Nowadays, most companies ask for system design knowledge in their interviews. In this book, you will learn the fundamentals of a cloud system, how to make it scalable, and how to build robust systems. Instead of learning tools, focus on the concepts so you don't waste time trying to learn everything. The Systems design interview became very popular in the market nowadays. After the big techs Google, Meta, Microsoft, Amazon and other companies started using it, all the other companies are doing the same. That’s why knowing how the systems design interview works is key for you to get a good job. The main objective of this book is to give you a solid foundational knowledge about Systems Design so you can learn any new technology faster. Programmers are not what the market wants anymore. Nowadays, companies are looking for software engineers, this means, a developer who can analyze a problem and come up with effective solutions. The ability of articulating a solution, creating good quality code, talking to coworkers, preparing documentation with architecture solutions, designing database, create performant and scalable systems are the skills companies are looking for. Some years ago, knowing a Java framework was enough for the market but the scenario has changed. That’s why the most powerful skill any software can have is the ability to learn, the ability to be a software engineer.

Название: jаvascript for hackers : Learn to think like a hacker
Автор: Gareth Heyes
Издательство: Leanpub
Год: 2024-01-25
Язык: английский
Формат: pdf (true), mobi, epub
Размер: 10.1 MB

Learn how to find interesting behaviour and flaws in jаvascript. Reading this book you will find the latest and greatest techniques for hacking jаvascript and generating XSS payloads. Includes ways to construct jаvascript using only +[]()! characters. Never heard of DOM Clobbering? This book has all the details. Have you ever wondered how a hacker approaches finding flaws in the browser and jаvascript? This book shares the thought processes and gives you tools to find your own flaws. It shares the basics of jаvascript hacking, then dives in and explains how to construct jаvascript payloads that don't use parentheses. Once you’ve got your chosen environment set up the next step is to set a goal. If you have no goal you can be staring at a blank page not getting anywhere. A goal enables you to make sure you’re always trying something and it can be flexible too. For instance one of my goals was “execute jаvascript without parentheses”. If you’ve set a good goal it will almost likely never end and good ones also mutate into another goal for example the goal I mentioned earlier mutated into “execute jаvascript functions without parentheses and pass arguments”. Now you can see how these two goals are useful because now you have a clear idea what you have to do and you can abuse jаvascript features to achieve that goal.

Название: Validity, Reliability, and Significance: Empirical Methods for NLP and Data Science, 2nd Edition
Автор: Stefan Riezler, Michael Hagmann
Издательство: Springer
Год: 2024
Страниц: 179
Язык: английский
Формат: pdf (true), epub
Размер: 25.7 MB

This book introduces empirical methods for Machine Learning with a special focus on applications in Natural Language Processing (NLP) and Data Science. The authors present problems of validity, reliability, and significance and provide common solutions based on statistical methodology to solve them. The book focuses on model-based empirical methods where data annotations and model predictions are treated as training data for interpretable probabilistic models from the well-understood families of generalized additive models (GAMs) and linear mixed effects models (LMEMs). Based on the interpretable parameters of the trained GAMs or LMEMs, the book presents model-based statistical tests such as a validity test that allows for the detection of circular features that circumvent learning. Furthermore, the book discusses a reliability coefficient using variance decomposition based on random effect parameters of LMEMs. Lastly, a significance test based on the likelihood ratios of nested LMEMs trained on the performance scores of two machine learning models is shown to naturally allow the inclusion of variations in meta-parameter settings into hypothesis testing, and further facilitates a refined system comparison conditional on properties of input data. The book is self-contained with an appendix on the mathematical background of generalized additive models and linear mixed effects models as well as an accompanying webpage with the related R and Python code to replicate the presented experiments. The second edition also features a new hands-on chapter that illustrates how to use the included tools in practical applications.

Название: 150+ Python Pattern Programs: Top Python exercises to feed your creativity with if statements and loops (150+ Pattern Programs Book 1)
Автор: Hernando Abella
Издательство: Aluna Publishing House
Год: 2024
Страниц: 186
Язык: английский
Формат: pdf, azw3, epub (true), mobi
Размер: 10.1 MB

"The continued demand for Python skills across industries makes it a valuable asset for developers". Develop your creativity by creating beautiful asterisk patterns with if statements and loops using the Python language. this awesome language will allow you to feel more comfortable writing code because you will master the more essential topics of programming by practicing these techniques. If you are studying computer science at university, this book is perfect for you. They will require you to create these types of patterns that will be very useful to learn all types of advanced techniques and quickly master this wonderful language.

Название: Hands-On Differential Privacy: Introduction to the Theory and Practice Using OpenDP
Автор: Ethan Cowan, Michael Shoemate, Mayana Pereira
Издательство: O’Reilly Media, Inc.
Год: 2024
Страниц: 362
Язык: английский
Формат: True PDF, True/Retail EPUB
Размер: 12.6 MB

Many organizations today analyze and share large, sensitive datasets about individuals. Whether these datasets cover healthcare details, financial records, or exam scores, it's become more difficult for organizations to protect an individual's information through deidentification, anonymization, and other traditional statistical disclosure limitation techniques. This practical book explains how differential privacy (DP) can help. Authors Ethan Cowan, Michael Shoemate, and Mayana Pereira explain how these techniques enable data scientists, researchers, and programmers to run statistical analyses that hide the contribution of any single individual. You'll dive into basic DP concepts and understand how to use open source tools to create differentially private statistics, explore how to assess the utility/privacy trade-offs, and learn how to integrate differential privacy into workflows. The OpenDP Library is part of the larger OpenDP Project. OpenDP is a community effort to build a trustworthy suite of open-source tools for enabling privacy-protective analysis of sensitive personal data, focused on a library of algorithms for generating differentially private statistical releases. The target use cases for OpenDP are to enable government, industry, and academic institutions to safely and confidently share sensitive data to support scientifically oriented research and exploration in the public interest. We aim for OpenDP to flexibly grow with the rapidly advancing science of differential privacy, and be a pathway to bring the newest algorithmic developments to a wide array of practitioners. SmartNoise is a collaboration between Microsoft, Harvard’s Institute for Quantitative Social Science (IQSS), and the OpenDP Project. The project aims to connect solutions from the research community with the lessons learned from real-world deployments to make differential privacy broadly accessible. Building upon the foundation of the OpenDP Library, the SmartNoise SDK includes two Python packages: smartnoise-sql and smartnoise-synth.

Название: Java Essentials: From Beginner to Developer
Автор: Programming Hub
Издательство: Programming Hub
Год: 2024
Страниц: 141
Язык: английский
Формат: pdf, epub
Размер: 11.1 MB

Dive into the world of programming with "Java Essentials: From Beginner to Developer," your comprehensive guide to mastering Java and setting the foundation for a successful career in software development. This pivotal resource unfolds the vast expanse of Java programming, from its fundamental principles to advanced object-oriented techniques, making it an indispensable tool for both aspiring programmers and seasoned developers looking to refine their skills. This book, "Java Essentials," is designed to be an illuminating beacon for students embarking on their adventure into the realm of Computer Science and engineering. Intended particularly for those in their formative years of study, this book aims to lay down a strong foundation in Java programming, a language renowned for its reliability, efficiency, and wide applicability across platforms. Java stands as one of the pillars of modern software development, its importance underscored by its role in building enterprise-level applications, Android apps, web applications, and much more. The versatility and stability of Java make it an indispensable tool for aspiring programmers and seasoned developers alike. Whether your interests lie in web development, mobile app creation, software engineering, or even in the burgeoning fields of Big Data and IoT, Java will prove to be a steadfast ally on your journey.

Название: An Introduction to Knowledge Graphs
Автор: Umutcan Serles, Dieter Fensel
Издательство: Springer
Год: 2024
Страниц: 440
Язык: английский
Формат: pdf (true)
Размер: 28.4 MB

This textbook introduces the theoretical foundations of technologies essential for knowledge graphs. It also covers practical examples, applications and tools. Knowledge graphs are the most recent answer to the challenge of providing explicit knowledge about entities and their relationships by potentially integrating billions of facts from heterogeneous sources. The standard narrative about the recent history of Artificial Intelligence (AI) is that a combination of rapidly growing compute power and an immense increase in available data have caused a scaling explosion in AI that has led to the results in Machine Learning (ML) in the past decade. Indeed, this narrative is true, and the results of the ML explosion are clear for all to see, in the popular press, in scientific publications, and in real world applications, ranging from product recommendation to fraud detection and from chatbots to face recognition. What is rather less known to the general public, the popular press, and indeed in AI itself is that a similar scaling explosion has taken place in another area of AI. The main driver for this “other explosion” of size in AI has been the adoption of the knowledge graph model, combined with ontology languages that carefully balance expressivity against computational cost. Knowledge graphs now form the biggest knowledge bases ever built, and without a doubt, languages like RDF Schema and OWL are by far the most widely used knowledge representation languages in the history of AI. And these knowledge graphs have come of age. They are used in science, in public administration, in cultural heritage, in healthcare, and in a broad range of industries, ranging from manufacturing to financial services and from search engines to pharmaceuticals. This textbook is intended for graduate courses covering knowledge graphs. Besides students in knowledge graph, Semantic Web, database, or information retrieval classes, also advanced software developers for Web applications or tools for Web data management will learn about the foundations and appropriate methods.

Название: Algorithms: Big Data, Optimization Techniques, Cyber Security
Автор: Sushil C. Dimri, Abhay Saxena, Bhuvan Unhelkar
Издательство: De Gruyter
Год: 2024
Страниц: 383
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Algorithms are ubiquitous in the contemporary technological world, and they ultimately consist of finite sequences of instructions used to accomplish tasks with necessary input values. This book analyses the top performing algorithms in areas as diverse as Big Data, Artificial Intelligence, Optimization Techniques and Cloud & Cyber Security Systems in order to explore their power and limitations. Algorithms play a vital role in all sciences, especially in Computer Science. We are always in search of efficient algorithms that give the results in less amount of time and that consume less space for large input size. Cybersecurity and Big Data are two prominent areas of Computer Science, where the role of algorithms is vital. Algorithms provide the best security mechanism to protect the data from all types of attacks. There are several types of attacks such as threats to data safety, data security, and on the mechanisms to store and retrieve the data. Optimization is a common term used almost everywhere, including our daily life. This book comprises chapters on the study of different algorithms used in cybersecurity and Big Data, and also puts light on optimization methods used in Computer Science. A comparative study of different algorithms is also provided. This book is helpful to students, especially for those who work in areas of cybersecurity and Big Data.

Название: Getting Started with Pester 5
Автор: Owen Heaume
Издательство: Apress
Год: 2024
Страниц: 239
Язык: английский
Формат: pdf
Размер: 10.1 MB

Embark on a transformative journey into the world of Pester and elevate your PowerShell scripting to new heights of robustness, reliability, and efficiency. This comprehensive beginner's guide provides a hands-on approach to PowerShell testing using Pester, empowering you to enhance your code quality with confidence. The book starts with an introduction to Pester, the powerful testing and mocking framework for PowerShell. You'll gain a solid foundation by exploring Pester's fundamental building blocks. The book guides you through the phases of Pester, helping you write your initial Pester code. You will dive into topics such as tags, assertions with Should and parameter filters, and you will explore advanced techniques such as Test Drive and Test Registry. Pester, in the realm of PowerShell, is not just a testing framework; it’s a game-changer. Imagine having a reliable assistant by your side, carefully checking your PowerShell scripts for errors, ensuring they perform as expected, and providing you with the peace of mind that your code is robust. That assistant is Pester. You will discover the art of mocking in Pester and master its advanced testing methodologies. You will gain insights into working without installed modules and seamlessly integrating Pester with Azure DevOps Pipeline for a streamlined testing process. After reading this book, you will have the knowledge and skills to initiate your own exploration into PowerShell testing with Pester. You will transform your scripts into reliable powerhouses, ensuring the resilience and quality of your PowerShell code.

Название: The Hitchhiker's Guide to Linear Models: Based on the famous R programming language
Автор: Mauricio Vargas Sepúlveda
Издательство: Leanpub
Год: 2024
Страниц: 187
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

For every exercise I did my best to connect the specific statistical concepts with R code, and every time I use linear algebra I connect it with a concrete R example. In this book you will not find something such as "this is left as an exercise to the reader". This book aims to get straight to the point, and the only thing I assume here is that you have used spreadsheets at some point and that you are motivated to estimate linear models in R. Here I do not assume that you know how to install R or the basics of the R programming language. This books contains no proofs. I tried to replace those with multiple examples consisting in analyzing my own experiments, such as throwing a tennis ball and measuring the time it takes to hit the ground from different heights, and another where I got two thermometers and measured the temperature outside a building at the same time of the day on different days.