It comprehensively presents the material necessary for understanding the internal architecture as well as system design aspects of Intel’s legendary 8085 and 8086 microprocessors and Intel’s 8051 and 8096 microcontrollers.The book throughout maintains an appropriate balance between the basic concepts and the skill sets needed for system design. Besides, the book lucidly explains the hardware architecture, the instruction set and programming, support chips, peripheral interfacing, and cites several relevant examples to help the readers develop a complete understanding of industrial application projects. Several system design case studies are included to reinforce the concepts discussed.With exhaustive coverage and practical approach, the book would be indispensable to undergraduate students of Electrical and Electronics, Electronics and Communication, and Electronics and Instrumentation Engineering. It can be used for a variety of courses in Microprocessors, Microcontrollers, and Embedded System Design.The second edition of the book introduces additional topics like I/O interfacing and programming, serial interface programming, delay programming using 8086 and 8051. Besides, many more examples and case studies have been added.Contents:Preface • Preface to the First EditionAcknowledgements1. System Design Using Microprocessor2. What a Microprocessor Is3. Intel 8085 Microprocessor—Hardware Architecture4. Intel 8085 Microprocessor—Instruction Set and Programming5. Intel 8086—Hardware Architecture6. Intel 8086 Microprocessor—Instruction Set and Programming7. Microprocessor—Peripheral Interfacing8. System Design Using Intel 8085 and Intel 8086 Microprocessors—Case Studies9. Intel 8051 Microcontroller—Hardware Architecture10. Intel 8051 Microcontroller—Instruction Set and Programming11. The 8051 Microcontroller-Based System Design—Case Studies12. Intel 8096 Microcontroller—Hardware Architecture13. Intel 8096 Microcontroller—Instruction Set and Programming14. The 8096 Microcontroller-Based System Design—Case StudiesAppendices • Index

    Scholars and students need to be able to analyze, design and curate information into useful tools of communication, insight and understanding. This book is the starting point in learning the process and skills of data visualization, teaching the concepts and skills of how to present data and inspiring effective visual design. Benefits of this book: A flexible step-by-step journey that equips you to achieve great data visualization.A curated collection of classic and contemporary examples, giving illustrations of good and bad practice Examples on every page to give creative inspiration Illustrations of good and bad practice show you how to critically evaluate and improve your own work Advice and experience from the best designers in the field Loads of online practical help, checklists, case studies and exercises make this the most comprehensive text available

    If you're working with a relational database--whether you're writing applications, performing administrative tasks, or generating reports--you need to know how to interact with your data. Even if you are using a tool that generates SQL for you, such as a reporting tool, there may still be cases where you need to bypass the automatic generation feature and write your own SQL statements.To help you attain this fundamental SQL knowledge, look to "Learning SQL," an introductory guide to SQL, designed primarily for developers just cutting their teeth on the language."Learning SQL" moves you quickly through the basics and then on to some of the more commonly used advanced features. Among the topics discussed: The history of the computerized databaseSQL Data Statements--those used to create, manipulate, and retrieve data stored in your database; example statements include select, update, insert, and deleteSQL Schema Statements--those used to create database objects, such as tables, indexes, and constraintsHow data sets can interact with queriesThe importance of subqueriesData conversion and manipulation via SQL's built-in functionsHow conditional logic can be used in Data StatementsBest of all, "Learning SQL" talks to you in a real-world manner, discussing various platform differences that you're likely to encounter and offering a series of chapter exercises that walk you through the learning process. Whenever possible, the book sticks to the features included in the ANSI SQL standards. This means you'll be able to apply what you learn to any of several different databases; the book covers MySQL, Microsoft SQL Server, and Oracle Database, but the features and syntax should apply just as well (perhaps with some tweaking) to IBM DB2, Sybase Adaptive Server, and PostgreSQL.Put the power and flexibility of SQL to work. With "Learning SQL" you can master this important skill and know that the SQL statements you write are indeed correct.

    "More concretely," the authors explain, "it is the controlled manipulation of mathematical formulas, using a collection of techniques for solving problems."

    You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data--Storytelling with Data will give you the skills and power to tell it!

    But, if you type in "white girls," the results are radically different. The suggested porn sites and un-moderated discussions about "why black women are so sassy" or "why black women are so angry" presents a disturbing portrait of black womanhood in modern society.In Algorithms of Oppression, Safiya Umoja Noble challenges the idea that search engines like Google offer an equal playing field for all forms of ideas, identities, and activities. Data discrimination is a real social problem; Noble argues that the combination of private interests in promoting certain sites, along with the monopoly status of a relatively small number of Internet search engines, leads to a biased set of search algorithms that privilege whiteness and discriminate against people of color, specifically women of color.Through an analysis of textual and media searches as well as extensive research on paid online advertising, Noble exposes a culture of racism and sexism in the way discoverability is created online. As search engines and their related companies grow in importance - operating as a source for email, a major vehicle for primary and secondary school learning, and beyond - understanding and reversing these disquieting trends and discriminatory practices is of utmost importance.An original, surprising and, at times, disturbing account of bias on the internet, Algorithms of Oppression contributes to our understanding of how racism is created, maintained, and disseminated in the 21st century.

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    Professors Gary Marcus and Ernest Davis have spent their careers at the forefront of AI research and have witnessed some of the greatest milestones in the field, but they argue that a computer winning in games like Jeopardy and go does not signal that we are on the doorstep of fully autonomous cars or superintelligent machines. The achievements in the field thus far have occurred in closed systems with fixed sets of rules. These approaches are too narrow to achieve genuine intelligence. The world we live in is wildly complex and open-ended. How can we bridge this gap? What will the consequences be when we do? Marcus and Davis show us what we need to first accomplish before we get there and argue that if we are wise along the way, we won't need to worry about a future of machine overlords. If we heed their advice, humanity can create an AI that we can trust in our homes, our cars, and our doctor's offices. Reboot provides a lucid, clear-eyed assessment of the current science and offers an inspiring vision of what we can achieve and how AI can make our lives better.

    Polya, How to Solve It will show anyone in any field how to think straight. In lucid and appealing prose, Polya reveals how the mathematical method of demonstrating a proof or finding an unknown can be of help in attacking any problem that can be reasoned out--from building a bridge to winning a game of anagrams. Generations of readers have relished Polya's deft--indeed, brilliant--instructions on stripping away irrelevancies and going straight to the heart of the problem.

    Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

    R is one of the most powerful and flexible statistical software packages available, and enables the user to apply a wide variety of statistical methods ranging from simple regression to generalized linear modelling. Statistics: An Introduction using R is a clear and concise introductory textbook to statistical analysis using this powerful and free software, and follows on from the success of the author's previous best-selling title Statistical Computing. * Features step-by-step instructions that assume no mathematics, statistics or programming background, helping the non-statistician to fully understand the methodology. * Uses a series of realistic examples, developing step-wise from the simplest cases, with the emphasis on checking the assumptions (e.g. constancy of variance and normality of errors) and the adequacy of the model chosen to fit the data. * The emphasis throughout is on estimation of effect sizes and confidence intervals, rather than on hypothesis testing. * Covers the full range of statistical techniques likely to be need to analyse the data from research projects, including elementary material like t-tests and chi-squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. * Includes numerous worked examples and exercises within each chapter. * Accompanied by a website featuring worked examples, data sets, exercises and solutions: http: //www.imperial.ac.uk/bio/research/crawl... Statistics: An Introduction using R is the first text to offer such a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a broad range of disciplines. It is primarily aimed at undergraduate students in medicine, engineering, economics and biology - but will also appeal to postgraduates who have not previously covered this area, or wish to switch to using R.

    This concise book shows you how to use the API to perform set operations with the DbSet class, handle change tracking and resolve concurrency conflicts with the Change Tracker API, and validate changes to your data with the Validation API.With DbContext, you’ll be able to query and update data, whether you’re working with individual objects or graphs of objects and their related data. You’ll find numerous C# code samples to help you get started. All you need is experience with Visual Studio and database management basics.Use EF’s query capabilities to retrieve data, and use LINQ to sort and filter dataLearn how to add new data, and change and delete existing dataUse the Change Tracker API to access information EF keeps about the state of entity instancesControl change tracking information of entities in disconnected scenarios, including NTier applicationsValidate data changes before they’re sent to the database, and set up validation rulesBypass EF’s query pipeline and interact directly with the database

    "Machine learning," the process of automating tasks once considered the domain of highly-trained analysts and mathematicians, is the key to efficiently extracting useful information from this sea of raw data. Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. In it, the author uses the flexible Python programming language to show how to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.

    With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems.Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution.Model data with the Cypher query language and property graph modelLearn best practices and common pitfalls when modeling with graphsPlan and implement a graph database solution in test-driven fashionExplore real-world examples to learn how and why organizations use a graph databaseUnderstand common patterns and components of graph database architectureUse analytical techniques and algorithms to mine graph database information

    It illustrates little-known features and techniques for getting the most from the Tableau toolset, supporting the needs of the business analysts who use the product as well as the data and IT managers who support it.This comprehensive guide covers the core feature set for data analytics, illustrating best practices for creating and sharing specific types of dynamic data visualizations. Featuring a helpful full-color layout, the book covers analyzing data with Tableau Desktop, sharing information with Tableau Server, understanding Tableau functions and calculations, and Use Cases for Tableau Software.Includes little-known, as well as more advanced features and techniques, using detailed, real-world case studies that the author has developed as part of his consulting and training practice Explains why and how Tableau differs from traditional business information analysis tools Shows you how to deploy dashboards and visualizations throughout the enterprise Provides a detailed reference resource that is aimed at users of all skill levels Depicts ways to leverage Tableau across the value chain in the enterprise through case studies that target common business requirements Endorsed by Tableau Software Tableau Your Data shows you how to build dynamic, best-of-breed visualizations using the Tableau Software toolset.