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Acclaimed professor Craig A. Evans gives a thoroughly researched and colorfully illustrated overview of the Dead Sea Scrolls and their importance for Christianity.
In today’s pluralistic society, not every approach to sharing the gospel will work with all people. Being ready to give reasons for the hope we have in Christ means understanding the contextual framework of the people we are addressing. In the Holman QuickSource Guide to Christian Apologetics, Renaissance man Doug Powell defends the Christian faith in a new key; taking timehonored approaches in apologetics and freshly presenting them for a new generation. Chapters include: 1. What Is Apologetics? 2. The Cosmological Argument for God’s Existence 3. The Teleological Argument for God’s Existence 4. The Axiological Argument for God’s Existence 5. Which God Exists? 6. Where Did the New Testament Come From? 7. Is the New Testament Reliable? 8. ExtraBiblical Evidence for Jesus 9. Is the Old Testament Reliable? 10. The Fulfillment of Prophecy 11. What About Miracles? 12. Was Jesus Raised from Death? 13. Did Jesus Claim to Be God? Is He the Only Way? 14. How can God allow Evil, Pain, and Suffering?
If you've been wanting to get a better understanding of the bible, even if you've been reading it for years - this is the resource for you! This QuickSource Guide features one-sentence summaries, timelines, key terms, colorful maps and charts, quick-hitting details on who wrote what and why, and so much more.
Book six in a greatly successful, visually-driven yet content-rich reference series, the Holman QuickSource™ Guide to Understanding Jesus takes a close and clear look at Christ in five parts: (1) His Old Testament background, (2) His life on Earth, (3) the Cross, (4) His teachings, and (5) His followers. Author Jeremy Howard, an apologetics expert inspired by the engaging style of C. S. Lewis, equips Christians with answers to deep and challenging spiritual questions while also helping seekers and unbelievers see Jesus for who He really is. In support, the book includes more than two hundred color photographs and illustrations.
Discover the power of location data to build effective, intelligent data models with Geospatial ecosystems Key FeaturesManipulate location-based data and create intelligent geospatial data modelsBuild effective location recommendation systems used by popular companies such as UberA hands-on guide to help you consume spatial data and parallelize GIS operations effectivelyBook Description Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease. What you will learnLearn how companies now use location dataSet up your Python environment and install Python geospatial packagesVisualize spatial data as graphsExtract geometry from spatial dataPerform spatial regression from scratchBuild web applications which dynamically references geospatial dataWho this book is for Data Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.
With its process-oriented rhetoric, provocative thematic reader, up-to-date research manual, and comprehensive handbook, The Bedford Guide for College Writers gives your students the tools they need to succeed as writers -- all in one book. Each of the book's four main components has been carefully developed to provide an engaging, well-coordinated guide for student writers. This edition's new, more open design and sharper focus on active learning do even more to help students develop transferable skills. The Bedford Guide for College Writers prepares students to be the confident, resourceful, and independent writers they will need to be.
This book focuses on a subtopic of explainable AI (XAI) called explainable agency (EA), which involves producing records of decisions made during an agent’s reasoning, summarizing its behavior in human-accessible terms, and providing answers to questions about specific choices and the reasons for them. We distinguish explainable agency from interpretable machine learning (IML), another branch of XAI that focuses on providing insight (typically, for an ML expert) concerning a learned model and its decisions. In contrast, explainable agency typically involves a broader set of AI-enabled techniques, systems, and stakeholders (e.g., end users), where the explanations provided by EA agents are best evaluated in the context of human subject studies. The chapters of this book explore the concept of endowing intelligent agents with explainable agency, which is crucial for agents to be trusted by humans in critical domains such as finance, self-driving vehicles, and military operations. This book presents the work of researchers from a variety of perspectives and describes challenges, recent research results, lessons learned from applications, and recommendations for future research directions in EA. The historical perspectives of explainable agency and the importance of interactivity in explainable systems are also discussed. Ultimately, this book aims to contribute to the successful partnership between humans and AI systems. Features: Contributes to the topic of explainable artificial intelligence (XAI) Focuses on the XAI subtopic of explainable agency Includes an introductory chapter, a survey, and five other original contributions