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Exam Prep for Bundle New Perspectives on Microsoft Access 2013 Introductory SAM 2013 Assessment Training and Projects V1 0 Multi Term Printed Access Card Microsoft Access 2013 CourseNotes
|Author||: Just the Facts101|
Your text simplified as the essential facts to prepare you for your exams. Over 2,000 higly probable test items.
|Author||: Richard S. Sutton,Andrew G. Barto|
|Editor||: MIT Press|
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
|Author||: Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong|
|Editor||: Cambridge University Press|
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
|Author||: Tom Kendrick|
Winner of the Project Management Institute’s David I. Cleland Project Management Literature Award 2010 It’s no wonder that project managers spend so much time focusing their attention on risk identification. Important projects tend to be time constrained, pose huge technical challenges, and suffer from a lack of adequate resources. Identifying and Managing Project Risk, now updated and consistent with the very latest Project Management Body of Knowledge (PMBOK)® Guide, takes readers through every phase of a project, showing them how to consider the possible risks involved at every point in the process. Drawing on real-world situations and hundreds of examples, the book outlines proven methods, demonstrating key ideas for project risk planning and showing how to use high-level risk assessment tools. Analyzing aspects such as available resources, project scope, and scheduling, this new edition also explores the growing area of Enterprise Risk Management. Comprehensive and completely up-to-date, this book helps readers determine risk factors thoroughly and decisively...before a project gets derailed.
|Author||: Nigel Campbell,Henk Cazemier,Robert Hatfield,Martin Petitclerc,Glen Seeds,Jason Tavoularis,IBM Redbooks|
|Editor||: IBM Redbooks|
This IBM® Redbooks® publication explains how IBM Cognos® Business Intelligence (BI) administrators, authors, modelers, and power users can use the dynamic query layer effectively. It provides guidance for determining which technology within the dynamic query layer can best satisfy your business requirements. Administrators can learn how to tune the query service effectively and preferred practices for managing their business intelligence content. This book includes information about metadata modeling of relational data sources with IBM Cognos Framework Manager. It includes considerations that can help you author high-performing applications that satisfy analytical requirements of users. This book provides guidance for troubleshooting issues related to the dynamic query layer of Cognos BI. Related documents: Solution Guide : Big Data Analytics with IBM Cognos BI Dynamic Query Blog post : IBM Cognos Dynamic Query Extensibility
|Author||: Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani|
|Editor||: Springer Science & Business Media|
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
|Author||: Peter Harrington|
|Editor||: Simon and Schuster|
Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce
|Author||: Paolo Bruni,Rafael Henrique Vedovatto Bindillati,James Kelly,Rick Long,Acacio Ricardo Gomes Pessoa,IBM Redbooks|
|Editor||: IBM Redbooks|
Over the last few years, IBM® IMSTM and IMS tools have been modernizing the interfaces to IMS and the IMS tools to bring them more in line with the current interface designs. As the mainframe software products are becoming more integrated with the Windows and mobile environments, a common approach to interfaces is becoming more relevant. The traditional 3270 interface with ISPF as the main interface is no longer the only way to do some of these processes. There is also a need to provide more of a common looking interface so the tools do not have a product-specific interface. This allows more cross product integration. Eclipse and web-based interfaces being used in a development environment, tooling using those environments provides productivity improvements in that the interfaces are common and familiar. IMS and IMS tools developers are making use of those environments to provide tooling that will perform some of the standard DBA functions. This book will take some selected processes and show how this new tooling can be used. This will provide some productivity improvements and also provide a more familiar environment for new generations DBAs. Some of the functions normally done by DBA or console operators can now be done in this eclipse-based environment by the application developers. This means that the need to request these services from others can be eliminated. This IBM Redbooks® publication examines specific IMS DBA processes and highlights the new IMS and IMS tools features, which show an alternative way to accomplish those processes. Each chapter highlights a different area of the DBA processes like: PSB creation Starting/stopping a database in an IMS system Recovering a database Cloning a set of databases
|Author||: Leslie Chan|
|Editor||: Perspectives on Open Access|
A fascinating look at Open Science and the democratization of knowledge in international development and social transformation.
|Author||: Lauren Dundes|
In this volume of 15 articles, contributors from a wide range of disciplines present their analyses of Disney movies and Disney music, which are mainstays of popular culture. The power of the Disney brand has heightened the need for academics to question whether Disney’s films and music function as a tool of the Western elite that shapes the views of those less empowered. Given its global reach, how the Walt Disney Company handles the role of race, gender, and sexuality in social structural inequality merits serious reflection according to a number of the articles in the volume. On the other hand, other authors argue that Disney productions can help individuals cope with difficult situations or embrace progressive thinking. The different approaches to the assessment of Disney films as cultural artifacts also vary according to the theoretical perspectives guiding the interpretation of both overt and latent symbolic meaning in the movies. The authors of the 15 articles encourage readers to engage with the material, showcasing a variety of views about the good, the bad, and the best way forward.
|Author||: Alvin C. Rencher,G. Bruce Schaalje|
|Editor||: John Wiley & Sons|
The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.
|Author||: Karan Singh,Corey C Bryant,Jonathan Cottrell,Gillian Gainsford,Saheem Granados,Robert Green,Diane Lia,Nilesh T Patel,John M Walsh,IBM Redbooks|
|Editor||: IBM Redbooks|
This IBM® Redbooks® publication examines the IBM Tivoli® Directory Server for z/OS®. IBM Tivoli Directory Server is a powerful Lightweight Directory Access Protocol (LDAP) infrastructure that provides a foundation for deploying comprehensive identity management applications and advanced software architectures. This publication provides an introduction to the IBM Tivoli Directory Server for z/OS that provides a brief summary of its features and a examination of the possible deployment topologies. It discusses planning a deployment of IBM Tivoli Directory Server for z/OS, which includes prerequisites, planning considerations, and data stores, and provides a brief overview of the configuration process. Additional chapters provide a detailed discussion of the IBM Tivoli Directory Server for z/OS architecture that examines the supported back ends, discusses in what scenarios they are best used, and provides usage examples for each back end. The discussion of schemas breaks down the schema and provides guidance on extending it. A broad discussion of authentication, authorization, and security examines the various access protections, bind mechanisms, and transport security available with IBM Tivoli Directory Server for z/OS. This chapter also provides an examination of the new Password Policy feature. Basic and advanced replication topologies are also covered. A discussion on plug-ins provides details on the various types of plug-ins, the plug-in architecture, and creating a plug-in, and provides an example plug-in. Integration of IBM Tivoli Directory Server for z/OS into the IBM Workload Manager environment is also covered. This publication also provides detailed information about the configuration of IBM Tivoli Directory Server for z/OS. It discusses deploying IBM Tivoli Directory Server for z/OS on a single system, with examples of configuring the available back ends. Configuration examples are also provided for deploying the server in a Sysplex, and for both basic and advanced replication topologies. Finally it provides guidance on monitoring and debugging IBM Tivoli Directory Server for z/OS.
|Author||: Jules S. Damji,Brooke Wenig,Tathagata Das,Denny Lee|
|Editor||: O'Reilly Media|
Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow
|Author||: Andreas Horni,Kai Nagel,Kay W. Axhausen|
|Editor||: Ubiquity Press|
The MATSim (Multi-Agent Transport Simulation) software project was started around 2006 with the goal of generating traffic and congestion patterns by following individual synthetic travelers through their daily or weekly activity programme. It has since then evolved from a collection of stand-alone C++ programs to an integrated Java-based framework which is publicly hosted, open-source available, automatically regression tested. It is currently used by about 40 groups throughout the world. This book takes stock of the current status. The first part of the book gives an introduction to the most important concepts, with the intention of enabling a potential user to set up and run basic simulations. The second part of the book describes how the basic functionality can be extended, for example by adding schedule-based public transit, electric or autonomous cars, paratransit, or within-day replanning. For each extension, the text provides pointers to the additional documentation and to the code base. It is also discussed how people with appropriate Java programming skills can write their own extensions, and plug them into the MATSim core. The project has started from the basic idea that traffic is a consequence of human behavior, and thus humans and their behavior should be the starting point of all modelling, and with the intuition that when simulations with 100 million particles are possible in computational physics, then behavior-oriented simulations with 10 million travelers should be possible in travel behavior research. The initial implementations thus combined concepts from computational physics and complex adaptive systems with concepts from travel behavior research. The third part of the book looks at theoretical concepts that are able to describe important aspects of the simulation system; for example, under certain conditions the code becomes a Monte Carlo engine sampling from a discrete choice model. Another important aspect is the interpretation of the MATSim score as utility in the microeconomic sense, opening up a connection to benefit cost analysis. Finally, the book collects use cases as they have been undertaken with MATSim. All current users of MATSim were invited to submit their work, and many followed with sometimes crisp and short and sometimes longer contributions, always with pointers to additional references. We hope that the book will become an invitation to explore, to build and to extend agent-based modeling of travel behavior from the stable and well tested core of MATSim documented here.
The UNESCO Training Manual for the Protection of the Underwater Cultural Heritage in Latin America and the Caribbean
|Author||: Netherlands. Ministry of Education, Culture and Science. Cultural Heritage Agency,UNESCO|
|Editor||: UNESCO Publishing|
|Author||: Paolo Bruni,Felipe Bortoletto,Adrian Burke,Cathy Drummond,Yasuhiro Ohmori,IBM Redbooks|
|Editor||: IBM Redbooks|
This IBM® Redbooks® publication discusses in detail the facilities of DB2® for z/OS®, which allow complete monitoring of a DB2 environment. It focuses on the use of the DB2 instrumentation facility component (IFC) to provide monitoring of DB2 data and events and includes suggestions for related tuning. We discuss the collection of statistics for the verification of performance of the various components of the DB2 system and accounting for tracking the behavior of the applications. We have intentionally omitted considerations for query optimization; they are worth a separate document. Use this book to activate the right traces to help you monitor the performance of your DB2 system and to tune the various aspects of subsystem and application performance.
|Author||: Truth and Reconciliation Commission of Canada|
|Editor||: James Lorimer & Company|
This is the Final Report of Canada's Truth and Reconciliation Commission and its six-year investigation of the residential school system for Aboriginal youth and the legacy of these schools. This report, the summary volume, includes the history of residential schools, the legacy of that school system, and the full text of the Commission's 94 recommendations for action to address that legacy. This report lays bare a part of Canada's history that until recently was little-known to most non-Aboriginal Canadians. The Commission discusses the logic of the colonization of Canada's territories, and why and how policy and practice developed to end the existence of distinct societies of Aboriginal peoples. Using brief excerpts from the powerful testimony heard from Survivors, this report documents the residential school system which forced children into institutions where they were forbidden to speak their language, required to discard their clothing in favour of institutional wear, given inadequate food, housed in inferior and fire-prone buildings, required to work when they should have been studying, and subjected to emotional, psychological and often physical abuse. In this setting, cruel punishments were all too common, as was sexual abuse. More than 30,000 Survivors have been compensated financially by the Government of Canada for their experiences in residential schools, but the legacy of this experience is ongoing today. This report explains the links to high rates of Aboriginal children being taken from their families, abuse of drugs and alcohol, and high rates of suicide. The report documents the drastic decline in the presence of Aboriginal languages, even as Survivors and others work to maintain their distinctive cultures, traditions, and governance. The report offers 94 calls to action on the part of governments, churches, public institutions and non-Aboriginal Canadians as a path to meaningful reconciliation of Canada today with Aboriginal citizens. Even though the historical experience of residential schools constituted an act of cultural genocide by Canadian government authorities, the United Nation's declaration of the rights of aboriginal peoples and the specific recommendations of the Commission offer a path to move from apology for these events to true reconciliation that can be embraced by all Canadians.
|Author||: Karim Benyekhlef,Jacquelyn Burkell,Jane Bailey,Fabien Gélinas|
How can we leverage digitization to improve access to justice without compromising the fundamental principles of our legal system? eAccess to Justice describes the challenges that come with the integration of technology into our courtrooms, and explores lessons learned from digitization projects from around the world.