This training course is a Linux version of the previous Practical Foundations of Windows Debugging, Disassembly, Reversing book. It also complements Accelerated Linux Core Dump Analysis training course.

Although the book skeleton is the same as its Windows predecessor, the content was revised entirely because of a different operating system, debugger (GDB), toolchain (GCC, assembler, linker), application binary interface, and even an assembly language flavor, AT&T.

Explore the latest and most comprehensive guide to securing your Cloud Native technology stack

Cloud Native Security delivers a detailed study into minimizing the attack surfaces found on today’s Cloud Native infrastructure. Throughout the work hands-on examples walk through mitigating threats and the areas of concern that need to be addressed. The book contains the information that professionals need in order to build a diverse mix of the niche knowledge required to harden Cloud Native estates.

This C# book has been a favorite of developers ever since the 1st edition came out in 2004. So you can be sure that this latest edition will deliver the professional skills you’re looking for today. In fact, it will teach you the C# essentials more easily than ever, as it shows you how to take advantage of the most recent releases of C#, .NET, and Visual Studio.

Apply decoupling patterns, properly test a decoupled project, and integrate a Django API with React, and Vue.js. This book covers decoupled architectures in Django, with Django REST framework and GraphQL. With practical and simple examples, you’ll see firsthand how, why, and when to decouple a Django project.

Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial intelligence technologies, and the privacy of the data used in the training and testing periods is also causing increasing concern among users.

This book provides practical information about web archives, offers inspiring examples for web archivists, raises new challenges, and shares recent research results about access methods to explore information from the past preserved by web archives.

Knowing our World: An Artificial Intelligence Perspective considers the methodologies of science, computation, and artificial intelligence to explore how we humans come to understand and operate in our world. While humankind’s history of articulating ideas and building machines that can replicate the activity of the human brain is impressive, Professor Luger focuses on understanding the skills that enable these goals.

Based on insights afforded by the challenges of AI design and program building, Knowing our World proposes a foundation for the science of epistemology. Taking an interdisciplinary perspective, the book demonstrates that AI technology offers many representational structures and reasoning strategies that support clarification of these epistemic foundations.

AI for Arts is a book for anyone fascinated by the man–machine connection, an unstoppable evolution that is intertwining us with technology in an ever-greater degree, and where there is an increasing concern that it will be technology that comes out on top. Thus, presented here through perhaps its most esoteric form, namely art, this unfolding conundrum is brought to its apex. What is left of us humans if artificial intelligence also surpasses us when it comes to art? The articulation of an artificial intelligence art manifesto is long overdue, so hopefully this book can fill a gap that will have repercussions not only for aesthetic and philosophical considerations but possibly more so for the development of artificial intelligence.

The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed, the focus being on the consistent and asymptotically normal (CAN) estimators. It is shown that for the probability models belonging to an exponential family and a Cramer family, the maximum likelihood estimators of the indexing parameters are CAN. The book describes some large sample test procedures, in particular, the most frequently used likelihood ratio test procedure. Various applications of the likelihood ratio test procedure are addressed, when the underlying probability model is a multinomial distribution. These include tests for the goodness of fit and tests for contingency tables. The book also discusses a score test and Wald’s test, their relationship with the likelihood ratio test and Karl Pearson’s chi-square test. An important finding is that, while testing any hypothesis about the parameters of a multinomial distribution, a score test statistic and Karl Pearson’s chi-square test statistic are identical.

There are many excellent books on Algorithms - why in the world we would write another one??? Because we feel that while these books excel in introducing algorithmic ideas, they have not yet succeeded in teaching you how to implement algorithms, the crucial computer science skill.

Our goal is to develop an Intelligent Tutoring System for learning algorithms through programming that can compete with the best professors in a traditional classroom. This MOOC book is the first step towards this goal written specifically for our Massive Open Online Courses (MOOCs) forming a specialization “Algorithms and Data Structures” on Coursera platform and a microMasters program on edX platform. Since the launch of our MOOCs in 2016, hundreds of thousands students enrolled in this specialization and tried to solve more than hundred algorithmic programming challenges to pass it. And some of them even got offers from small companies like Google after completing our specialization!