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Authors: Mayuri Mehta (2023) - This book presents an overview and several applications of explainable artificial intelligence (XAI). It covers different aspects related to explainable artificial intelligence, such as the need to make the AI models interpretable, how black box machine/deep learning models can be understood using various XAI methods, different evaluation metrics for XAI, human-centered explainable AI, and applications of explainable AI in health care, security surveillance, transportation, among other areas.
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Authors: Pam Baker (2025) - Generative AI tools capable of creating text, images, and even ideas seemingly out of thin air have exploded in popularity and sophistication. This valuable technology can assist in authoring short and long-form content, producing audio and video, serving as a research assistant, and tons of other professional and personal tasks. Generative AI For Dummies is your roadmap to using the world of artificial intelligence to enhance your personal and professional lives. You'll learn how to identify the best platforms for your needs and write the prompts that coax out the content you want. Written by the best-selling author of ChatGPT For Dummies, this book is the ideal place to start when you're ready to fully dive into the world of generative AI.
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Authors: Russ White (2014) - The Art of Network Architecture is the first book that places business needs and capabilities at the center of the process of architecting and evolving networks. Two leading enterprise network architects help you craft solutions that are fully aligned with business strategy, smoothly accommodate change, and maximize future flexibility.Russ White and Denise Donohue guide network designers in asking and answering the crucial questions that lead to elegant, high-value solutions. Carefully blending business and technical concerns, they show how to optimize all network interactions involving flow, time, and people.
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Authors: Luvai F. Motiwalla (2012) - This edition specifically:• provides several examples of real-world company issues that occurred while implementing enterprise systems;• provides a step-by-step learning process for students, using organized materials, and learning • focuses on a pedagogy that lays out concise learning goals and reinforces the concepts learned using cases, discussion questions, and exercises; andabout enterprise system implementations;• highlights issues within the implementation process that have implications for management.
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Authors: Rafael Ris-Ala (2023) - This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges
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Authors: Ulisses Braga-Neto (2021) - This book is a concise but thorough introduction to the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as deep neural networks and Gaussian process regression. The Second Edition is thoroughly revised, featuring a new chapter on the emerging topic of physics-informed machine learning and additional material on deep neural networks.
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Authors: Taurius Litvinavicius (2021) - This book will get you through not only the basics, but also some of the more advanced concepts of WPF in .NET 5.The book starts with basic concepts such as window, page, text box, and message box as well as a sequence of common events and event handling in WPF. You will learn how to use various elements in WPF and deal with them in .NET 5. You will understand how to work with files and access them in WPF along with binding and MVVM (Model-View-View-Model). You will learn how to retrieve data from APIs, work in XAML, and understand where design and style properties should be applied in WPF.
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Authors: John Durkin (1994) - -
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Authors: Aurélien Géron (2022) - Third edition, explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you've learned. Programming experience is all you need to get started: Use Scikit-learn to track an example ML project end to end; Explore several models, including support vector machines, decision trees, random forests, and ensemble methods; Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection.
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Authors: Andrew Troelsen (2021) - This essential classic provides a comprehensive foundation in the C# programming language and the framework it lives in. Now in its 10th edition, you will find the latest C# 9 and .NET 5 features served up with plenty of "behind the curtain" discussion designed to expand developers’ critical thinking skills when it comes to their craft. Coverage of ASP.NET Core, Entity Framework Core, and more, sits alongside the latest updates to the new unified .NET platform, from performance improvements to Windows Desktop apps on .NET 5, updates in XAML tooling, and expanded coverage of data files and data handling. Going beyond the latest features in C# 9, all code samples are rewritten for this latest release.
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