Databases / Data management Books
APress Pro Power BI Theme Creation
Book SynopsisUse JSON theme files to standardize the look of Power BI dashboards and reports. This book shows how you can create theme files using the Power BI Desktop application to define high-level formatting attributes for dashboards as well as how to tailor detailed formatting specifications for individual dashboard elements in JSON files. Standardize the look of your dashboards and apply formatting consistently over all your reports. The techniques in this book provide you with tight control over the presentation of all aspects of the Power BI dashboards and reports that you create.Power BI theme files use JSON (JavaScript Object Notation) as their structure, so the book includes a brief introduction to JSON as well as how it applies to Power BI themes. The book further includes a complete reference to all the current formatting definitions and JSON structures that are at your disposal for creating JSON theme files up to the May 2023 release of Power BI Desktop. Finally, the book includes dozTable of Contents1. Introduction to Power BI Themes2. Create and Customize a Theme In Power BI Desktop3. High-Level Theme Definition4. Default Visual Styles5. Object Visual Styles6. Card and Table Visual Styles7. Classic Chart Visual Styles8. Complex Chart Visual Styles9. Other Chart Visual Styles10. Maps11. Miscellaneous Visual Styles12. Dashboard Styling13. Cascading Styles
£46.74
Springer International Publishing AG Artificial Intelligence A Guide for Everyone
Book SynopsisEnterprises, as well as individuals, are racing to reap the benefits of AI. However, in most cases, they are doing so without understanding the technology or its implications and risks, which can be significant. Artificial Intelligence: A Guide for Everyone is a step in addressing that gap by providing information that readers can easily understand at every level. This book aims to provide useful information to those planning, developing, or using AI, which has the potential to transform industries and shape the future. Whether you are stepping into the world of AI for the first time or are a seasoned professional seeking deeper insights, this comprehensive guide ensures that both beginners and experienced individuals find value within its pages. Artificial Intelligence: A Guide for Everyone encompasses theoretical as well as practical aspects of AI across various industries and applications. It demystifies AI by explaining, in a language that non-techies can follow, its history, d
£23.74
Taylor & Francis Ltd Data Science Foundations
a huge range and FREE tracked UK delivery on ALL orders.
£45.99
United Nations Charter of the United Nations and Statute of the
Book SynopsisThe Charter of the United Nations was signed in 1945 by 51 countries representing all continents, paving the way for the creation of the United Nations on 24 October 1945. The Statute of the International Court of Justice forms part of the Charter. The aim of the Charter is to save humanity from war; to reaffirm human rights and the dignity and worth of the human person; to proclaim the equal rights of men and women and of nations large and small; and to promote the prosperity of all humankind. The Charter is the foundation of international peace and security.
£8.56
Taylor & Francis Ltd Questions in Dataviz
Book SynopsisThis book takes the reader through the process of learning and creating data visualisation, following a unique journey with questions every step of the way, ultimately discussing how and when to bend and break the rules to come up with creative, unique, and sometimes unconventional ideas. Each easy-to-follow chapter poses one key question and provides a selection of discussion points and relevant data visualisation examples throughout.Structured in three parts: Section I poses questions around some fundamental data visualisation principles, while Section II introduces more advanced questions, challenging perceived best practices and suggesting when rules are open to interpretation or there to be broken. The questions in Section III introduce further themes leading on to specific ideas and visualisation projects in more detail.Questions in Dataviz: A Design-Driven Process for Data Visualisation will appeal to any reader with an interest in creaTrade Review"It’s a common experience for newcomers in visualization to be a bit disoriented. Here are some questions we’ve all asked ourselves at some point: Am I doing things correctly? Should I use this type of chart or that other type? Is this color palette appropriate? Will my intended audience understand the point I’m trying to make with this graphic? Will they be able to use the graphic’s interface? Am I breaking any rule of visualization? And so many others. The difference between Neil Richards and the rest of us is that Neil has written an entire book about his posing those questions to himself, and about the journey towards trying to answer them. Spoiler alert: the journey is often circuitous, and sometimes lacks a clear destination. But who cares? The journey, and not where it leads, is what can make us wiser as professionals; the process of reasoning to disentangle complex design choices has a value of its own.Moreover, and perhaps more importantly, walking that path along someone as friendly and personable as Neil makes the experience exciting and joyful."– Alberto Cairo and Tamara Munzner, Series Editors, AK Peters Visualization Series"Questions in Dataviz is an amazing resource for data visualisation folks looking for different and more creative design ideas - instead of following the norms of business data visualisation it asks the questions that challenge conventional practices to inspire new ideas to develop your own style and data visualisation philosophy. Neil introduces us to the concepts, inspirations and designers that inspired him, and encourages you to ask questions to find your own design driven journey into more creative design-driven output."– Giorgia Lupi, Pentagram"Beyond technical skills, statistical knowledge, and creative talent, one of the most vital attributes in data visualisation is to be curious. Before a chart comes data. Before the data comes a question. Questions fuel one’s understanding about anything and in this super new book, Neil Richards eloquently demonstrates his amazing flair for being curious. He answers the questions he had – and that anyone else should have – about the journey towards successfully mastering data visualisation. He delightfully unpacks the whys, the why nots, and the hows of this complex subject, in a wonderfully engaging and perfectly nuanced way."– Andy Kirk, Visualising Data Ltd."Neil writes about the 'why' behind his own chart design decisions in an engaging way that will give any new practitioner a glimpse inside the brain of a data visualization designer, with examples that showcase how an individual designer's style evolves and changes over time. For the experienced practitioner, Neil's book offers a tour through the many questions about our motivations and design decisions in data visualization that have emerged over the past decade or more. In some ways, the ideas feel like a delightful highlights reel of debates and discussions born out on Twitter and in various slack channels, summarized neatly and without judgement around the ways we may come to different answers to those questions."– Amanda Makulec, Executive Director, Data Visualization Society"Neil is a luminary in the field and his work clearly pushes the boundaries of data visualization. This book will help people push past the "standard" chart types and consider different, alternative visualizations that they may not have considered before."– Jonathan Schwabish, Urban Institute and PolicyViz“When do we break the rules? What are the exceptions? What is the decision making process that goes into creating dataviz and how do you bend the universal principles based on specific circumstances? This book explores these questions in an open-minded way.”– Valentina D'Efilippo, Award-winning data designerTable of ContentsPreface. Author. Introduction. SECTION I First Questions. Chapter 1.1 Should the data drive the visualisation? Chapter 1.2 What’s in a colour? Chapter 1.3 What does data visualisation have in common with psychology? Chapter 1.4 Do data visualisations have to tell a story? Chapter 1.5 Is it OK to steal? Chapter 1.6 Is white space always your friend? Section II Challenging. Questions Chapter 2.1 Why do we visualise data? Chapter 2.2 Why do we visualise using triangles? Chapter 2.3 Does it matter if shapes overlap? Chapter 2.4 What is data humanism? Chapter 2.5 What is design-driven data? Chapter 2.6 Do we take data visualisation too seriously? Chapter 2.7 Why create unnecessary data visualisations? Chapter 2.8 When are several visualisations better than one? Chapter 2.9 What can I do when data is impossible to find? Section III Idea Questions. Chapter 3.1 What is the third wave of data visualisation? Chapter 3.2 What alternative ways are there for visualizing timelines? Chapter 3.3 Why do I use flowers to visualise data? Chapter 3.4 What are Data Portraits? Chapter 3.5 How can I take inspiration from album covers? Chapter 3.6 How many ways can you tile the United States? Chapter 3.7 Is it possible to tile the world? Chapter 3.8 Can you create visualisations using only numbers? Chapter 3.9 How do you visualise music? Chapter 3.10 What are Truchet tiles? Chapter 3.11 How do you create 31 visualisations in a month? Index.
£34.99
Manning Publications Bayesian Optimization in Action
Book SynopsisApply advanced techniques for optimising machine learning processes For machine learning practitioners confident in maths and statistics. Bayesian Optimization in Action shows you how to optimise hyperparameter tuning, A/B testing, and other aspects of the machine learning process, by applying cutting-edge Bayesian techniques. Using clear language, Bayesian Optimization helps pinpoint the best configuration for your machine-learning models with speed and accuracy. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult! Key features include: Train Gaussian processes on both sparse and large data sets Combine Gaussian processes with deep neural networks to make them flexible and expressive Find the most successful strategies for hyperparameter tuning Navigate a search space and identify high-performing regions Apply Bayesian Optimisation to practical use cases such as cost-constrained, multi-objective, and preference optimisation Use PyTorch, GPyTorch, and BoTorch to implement Bayesian optimisation You will get in-depth insights into how Bayesian optimisation works and learn how to implement it with cutting-edge Python libraries. The book's easy-to-reuse code samples will let you hit the ground running by plugging them straight into your own projects! About the technology Experimenting in science and engineering can be costly and time-consuming, especially without a reliable way to narrow down your choices. Bayesian Optimisation helps you identify optimal configurations to pursue in a search space. It uses a Gaussian process and machine learning techniques to model an objective function and quantify the uncertainty of predictions. Whether you're tuning machine learning models, recommending products to customers, or engaging in research, Bayesian Optimisation can help you make better decisions faster.
£43.69
John Wiley & Sons Inc Database Development For Dummies
Book SynopsisThe key to successful database development is accurate and appropriate modelling of the real-world system that will be placed on the computer. This guide describes two popular modelling methods, the entity-relationship model and the semantic object model.Table of ContentsIntroduction 1 Part I: Basic Concepts 7 Chapter 1: Database Processing 9 Chapter 2: Database Development 21 Part II: Data Modeling: What Should the Database Represent? 39 Chapter 3: The Users’ Model 41 Chapter 4: The Entity-Relationship Model 49 Chapter 5: The Semantic Object Model 67 Chapter 6: Determining What You Are Going to Do 89 Part III: Database Design 103 Chapter 7: The Relational Model 105 Chapter 8: Using an Entity-Relationship Model to Design a Database 129 Chapter 9: Using a Semantic Object Model to Design a Database 141 Part IV: Implementing a Database 159 Chapter 10: Using DBMS Tools to Implement a Database 161 Chapter 11: Addressing Bigger Problems with SQL Server 2000 199 Chapter 12: Using SQL to Implement a Database 217 Part V: Implementing a Database Application 229 Chapter 13: Using DBMS Tools to Implement a Database Application 231 Chapter 14: SQL and Database Applications 251 Part VI: Using Internet Technology with Database 257 Chapter 15: Database on Networks 259 Chapter 16: Database Security and Reliability 271 Part VII: The Part of Tens 281 Chapter 17: Ten Rules to Remember When Creating a Database 283 Chapter 18: Ten Rules to Remember When Creating a Database Application 289 Glossary 293 Index 305
£25.59
APress Pro DAX with Power BI
Book SynopsisLearn the intricate workings of DAX and the mechanics that are necessary to solve advanced Power BI challenges. This book is all about DAX (Data Analysis Expressions), the formula language used in Power BI-Microsoft''s leading self-service business intelligence application-and covers other products such as PowerPivot and SQL Server Analysis Services Tabular. You will learn how to leverage the advanced applications of DAX to solve complex tasks.Often a task seems complex due to a lack of understanding, or a misunderstanding of core principles, and how certain components interact with each other. The authors of this book use solutions and examples to teach you how to solve complex problems. They explain the intricate workings of important concepts such as Filter Context and Context Transition. You will learn how Power BI, through combining DAX building blocks (such as measures, table filtering, and data lineage), can yield extraordinary analytical power. Throughout Table of ContentsPart I: The FoundationChapter 1: DAX MechanicChapter 2: Data ModelingChapter 3: DAX LineagePart II: Core ConceptsChapter 4: This Weird Context ThingChapter 5: Filtering in DAXChapter 6: IteratorsChapter 7: Filters Using Measures Part III: DAX to Solve Advanced Everyday ProblemsChapter 8: Using DAX to Solve Advanced Reporting RequirementsChapter 9: Time IntelligenceChapter 10: Finding What's Not ThereChapter 11: Row Level SecurityPart IV: Debugging and OptimizationChapter 12: DAX StudioChapter 13: Query PlansChapter 14: Scale your Models
£42.49
Mike Murach & Associates Inc. Murach's MySQL, 3rd Edition
£50.14
Pearson Education (US) Microsoft Azure Data Solutions An Introduction
Book SynopsisDaniel A. Seara is an experienced software developer. He has more than 20 years as a technical instructor, developer, and development consultant. Daniel has worked as a software consultant in a wide range of companies in Argentina, Spain, and Peru. He has been asked by Peruvian Microsoft Consulting Services to help several companies in their migration path to .NET development. Daniel was Argentina's Microsoft regional director for four years and was the first nominated global regional director, a position he held for two years. He also was the manager of the Desarrollador Cinco Estrellas I (Five Star Developer) program, one of the most successful training projects in Latin America. Daniel held Visual Basic MVP status for more than 10 years, as well as SharePoint Server MVP status from 2008 until 2014. Additionally, Daniel is the founder and dean of Universidad. NET, the most-visited Spanish-Table of Contents1. Understanding Azure Data Solutions 2. Implementing Azure Data Storage Solutions 3. Managing and Developing Data Processing for Azure Data Solutions 4. Monitoring and Optimizing Azure Data Solutions
£32.29
MIT Press Ltd Mathematics of Big Data
Book Synopsis
£72.20
Technics Publications LLC The DAMA Dictionary of Data Management CDROM Over
Book Synopsis
£46.77
Cambridge University Press Big Data Over Networks
Book SynopsisUtilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data overTable of ContentsPart I. Mathematical Foundations: 1. Tensor models – solution methods and applications Shiqian Ma, Bo Jiang, Xiuzhen Huang and Shuzhong Zhang; 2. Sparsity-aware distributed learning Symeon Chouvardas, Yannis Kopsinis and Sergios Theodoridis; 3. Optimization algorithms for big data with application in wireless networks Mingyi Hong, Wei-Cheng Liao, Ruoyu Sun and Zhi-Quan Luo; 4. A unified distributed algorithm for non-cooperative games Jong-Shi Pang and Meisam Razaviyayn; Part II. Big Data over Cyber Networks: 5. Big data analytics systems Ganesh Ananthanarayanan and Ishai Menache; 6. Distributed big data storage in optical wireless networks Chen Gong, Zhengyuan Xu and Xiaodong Wang; 7. Big data aware wireless communication – challenges and opportunities Suzhi Bi, Rui Zhang, Zhi Ding and Shuguang Cui; 8. Big data processing for smart grid security Lanchao Liu, Zhu Han, H. Vincent Poor and Shuguang Cui; Part III. Big Data over Social Networks: 9. Big data: a new perspective on cities Riccardo Gallotti, Thomas Louail, Rémi Louf and Marc Barthelemy; 10. High dimensional network analytics: mapping topic networks in Twitter data during the Arab Spring Kathleen M. Carley, Wei Wei and Kenneth Joseph; 11. Social influence analysis in the big data era – a review Jianping Cao, Dongliang Duan, Liuqing Yang, Qingpeng Zhang, Senzhang Wang and Feiyue Wang; Part IV. Big Data over Biological Networks: 12. Inference of gene regulatory networks – validation and uncertainty Xiaoning Qian, Byung-Jun Yoon and Edward R Dougherty; 13. Inference of gene networks associated with the host response to infectious disease Zhe Gan, Xin Yuan, Ricardo Henao, Ephraim L. Tsalik and Lawrence Carin; 14. Gene-set-based inference of biological network topologies from big molecular profiling data Lipi Acharya and Dongxiao Zhu; 15. Large scale correlation mining for biomolecular network discovery Alfred Hero and Bala Rajaratnam.
£60.79
McGraw-Hill Education Fuzzy Logic Applications in Artificial
Book SynopsisFuzzy logic principles, practices, and real-world applicationsThis hands-on guide offers clear explanations of fuzzy logic along with practical applications and real-world examples. Written by an award-winning engineer, Fuzzy Logic: Applications in Artificial Intelligence, Big Data, and Machine Learning is aimed at improving competence and motivation in students and professionals alike.Inside, you will discover how to apply fuzzy logic in the context of pervasive digitization and big data across emerging technologies which require a very different man-machine relationship than the ones previously used in engineering, science, economics, and social sciences. Applications covered include intelligent energy systems with demand response, smart homes, electrification of transportation, supply chain efficiencies, smart cities, e-commerce, education, healthcare, and decarbonization.Serves as a classroom guide and as an on-the-job resource
£72.89
APress Getting Started with Oracle Cloud Free Tier
Book SynopsisIntermediate-Advanced user levelTable of ContentsIntroductionPart I. Getting Started1. Create an Account2. Identity and Access ManagementPart II. Infrastructure and Operations3. Basic Networking4. Compute Instances5. Storage6. Oracle Autonomous Linux7. Autonomous Databases8. Load Balancers9. Notifications and MonitoringPart III. Applications10. SQL Developer Web11. Oracle Application Express12. Oracle REST Data Services13. Deploy Multitiered Web Applications14. Oracle Machine Learning NotebooksPart IV. Next Steps15. Infrastructure as Code16. Account Management
£48.74
APress Data Lake Analytics on Microsoft Azure A
Book SynopsisBeginning-Intermediate user levelTable of ContentsChapter 1: Introduction and The Need of Data LakeChapter Goal: The chapter introduces the readers to the concept & need of a data lake in this big data environment.The chapter also covers how to create a data lake & architecture patterns to be followed for data lake analytics. No of pages 15 Sub -Topics 1. Relational and non-relation data stores 2. Base for data: relational and non-relational databases 3. Warehouses of data: data warehouses 4. Markets for data: data marts 5. Introduction to data lake 6. Need to create a data lake Chapter 2: Data Just Got Bigger Chapter Goal: Today, enterprises have mix of relational and non-relational stores. However, when it comes to analyzing all this data – there must be a neutral platform which can understand these types of data. This introduces us to modern world concepts of distributed data storage & processing. It also talks about data sciences & machine learning concepts & how they are revolutionizing the data analysis world. No of pages : 20 Sub - Topics: 1. Massively parallel processing, distributed data and spark the Hadoop 2. Distributed systems vs massively parallel processing systems (MPP) 3. Respective use cases for distributed and MPP systems 4. Science for data 5. Learning of machines 6. Overview of data analytics and advanced data analytics Chapter 3: Emergence of Cloud Lakes Chapter Goal: The chapter enlighten the users with multiple cloud-based technologies available which are scalable, agile and performance in terms of computation, storage & analytics options. It goes into details about the suggested architecture on Microsoft Azure to solve Modern data warehouse, analytics use cases. No of pages: 20 Sub - Topics: 1. Data travels to Cloud with added benefits 2. Overview of phases of data analytics architecture 3. Available products under each phase on Microsoft Azure Chapter 4: Phases in Managing Data Analytics Pipeline Chapter Goal: This chapter covers in-depth context of this book. After we understand the background, this chapter will provide understanding of what are the phases of building entire data analytics pipeline. All the phases discussed in this book are critical to understand and any analytics solution will adhere to this common principle some way or the other. In each phase, there are different solutions to cater respective issues. It covers the data life cycle from upstream to downstream applications. No of pages: 20 Sub - Topics: 1. Real time and batch mode data processing 2. Phases in data Management · Ingest · Store · Analytics · Visualization 3. Cloud data lake architecture patterns Chapter 5: Data Ingestion in the Lake Chapter Goal: The chapter talks about the limitations about the traditional storage & how the big data technologies has emerged as the champion in solving the limitations & changing the concepts of Extract, Transform & Load (ETL) to Extract, Load & Transform(ELT). No of pages: 20 Sub - Topics: 1. Traditional limitations, can big data help? 2. ETL now becomes ELT 3. Tools in cloud for data ingestion · Azure Data Factory on Microsoft Azure · SQL server integration services on-premise 4. Overview of partner solutions for ETL/ELT – Informatica PowerEdge Chapter 6: Data Storage & Farming Chapter Goal: The chapter shares with readers that how once the data is available in storage layers, how it can be grown & real time data storage & analysis needs can be catered, it also talks about batch & real time data processing & storage. No of pages: 20 Sub - Topics: 1. Grow the data 2. Role of Azure data lake store, Blob, relational and non-relational stores 3. Architecting the Lambda & Kappa 4. Manage storage for real time and batch processing Chapter 7: Analyzing the Bigger Data in Real Time Chapter Goal: Analysis of data is crucial for enterprises to get the business insights from the historic, present & future data to make descriptive, streaming & predictive analytics. In this chapter, we will specifically talk about real time analytics. Components required to perform real time analytics and how to optimize the cost using Azure PaaS solutions. No of pages: 30 Sub - Topics: 1. Need of real time analytics 2. Approach to build data analytics on data lake for real time processing 3. Leverage event hubs/IOT hubs as a queuing solution on Azure 4. Why Edge computing and digital twins are gaining limelight 5. Choice between PaaS vs IaaS solution for streaming data processing 6. PaaS – stream analytics or spark streaming 7. Infuse R and Python on real-time data analytics pipelines 8. Use cases for real time analytics Chapter 8: Analyzing the Bigger Data in Batch Mode Chapter Goal: Analysis of data is crucial for enterprises to get the business insights from the historic, present & future data to make descriptive, streaming & predictive analytics. Analytics can help companies identify new business opportunities and revenue streams which results in an increase in profits, new customers, and improved customer service. No of pages: 30 Sub - Topics: 9. Role of big data and massively parallel processing systems 10. Approach to build data analytics on data lake for batch processing 11. Approach to build data analytics solution for real time analytics 12. When to leverage HDInsight and Spark clusters 13. Infuse R and Python in data analytics pipelines 14. How it's different from conventional data warehousing and massively parallel processing solutions 15. Use cases for batch mode processing Chapter 9: Visualization and Other Downstream Choices Chapter Goal: Visualization of data is crucial for reporting& also to perform exploratory data analytics. The chapter talks about the visual elements like charts, graphs, and maps, data visualization tools which provide an accessible way to see and understand trends, outliers, and patterns in data No of pages: 10 Sub - Topics: 1. Visualizations tools – Power BI 2. Downstream applications – LOB applications, notification applications 3. Choice of data stores for downstream applications – Cosmos DB, Azure SQL Database Chapter 10: Summary of Data Lake components in Azure Chapter Goal: The chapter takes a dig at multiple azure components which makes its easy to create an enterprise data lake in cloud & talks about in details the usage of each No of pages: 20 Sub - Topics: 1. Azure data factory 2. Azure data lake storage 3. Azure HDInsight 4. Azure databricks 5. Azure data warehouse 6. Azure PowerBI Chapter 11: Conclusion Chapter Goal: The concluding chapter summarizes the information shared around the data lake in the book No of pages: 5
£29.99
APress Blockchain Enabled Applications
Book SynopsisLearn all about blockchain and its applications in cryptocurrency, healthcare, Internet of Things, finance, decentralized organizations, and more. Featuring case studies and practical insights, this book covers a unique mix of topics and offers insight into how to overcome hurdles that arise as the market and consumers grow accustomed to blockchain-based organizations and services.The book is divided into three major sections. The first section provides a historical background to blockchain technology. You will start with a historical context to financial capital markets when Bitcoin was invented, followed by mining protocols, the need for consensus, hardware mining, etc. Next, a formal introduction to blockchain is provided covering transaction workflow, role of decentralized network, and payment verification. Then, we dive deep into a different implementation of a blockchain: Ethereum. The main technical features, such as Ethereum Virtual Machine, are presented aTable of ContentsChapter 1: Behold the DeamersChapter Goal: Provide a backdrop for introducing blockhain and the basics of a decentralized appSub -Topics:1. Financial crisis of 2008, the origins of bitcoin2. Basics of private-public keys3. What is a block, how is a block created4. What's a blockchain-enabled application? What is a decentralized application?Chapter 2: Gold Rush: Mining BitcoinChapter Goal: Provide a technical introduction to mining and the mathematical background to hashes, block headers, and consensusSub -Topics:1. Overview of mining, why is mining necessary for Bitcoin2. What is consensus3. Components of a block and a block header (mining components)4. What are hashes and how are they used in Bitcoin5. Hardware for mining (the gold rush part refers to the arms race that happened in hardware mining field).Chapter 3: Foundations of a BlockchainChapter Goal: Provide a technical introduction to transaction workflow, a blockchain network, simple payment verification, merkel roots, and block identifiersSub -Topics:1. What is a block header (block identifiers)2. How does the network participate3. A transaction workflow 4. Unspent transaction outputs, transaction propagation5. Simple payment verification6. Merkel roots, blockchain forksChapter 4: Unpacking EthereumChapter Goal: Provide a technical introduction to Ethereum, the differences between a Bitcoin blockchain and Ethereum blockchain, internal states, Ethereum Virtual Machine, and dAppsSub -Topics:1. Overview of Ethereum 2. Proof of stake3. Accounts and contract model in Ethereum4. Global state, gas, internal storage5. Ethereum Virtual Machine6. Solidity programming language + Smart Contracts7. World Computer Model and components8. Blockchain as a service9. Decentralized apps10. Geth, MistChapter 5: Decentralized Organizations (DAOs)Chapter Goal: Provide a technical introduction to DAOs and Aragon for setting up a DAO, including updates to new implementations of decentralized organizations in 2020. Sub -Topics:1. What is a DAO 2. What is a blockchain organizations/companies3. Aragon-core and Kernel4. How do you make DAOs and other blockchain organizations using Aragon?5. How do you operate DAOs?Chapter 6: The DAO HackedChapter Goal: Provide an overview to the vulnerabilities in the original DAO model, the conditions that led up to the hack, and the consequences to security hardening since. Sub -Topics:1. Concept of a DAO building on Vitalik’s concepts2. Slock.it and its involvement in making the DAO 3. The Smart Contract for DAO4. The code vulnerability responsible for the hack5. Consequences of the hack6. Ethereum splitting into ETCChapter 7: Ethereum Tokens: High Performance Computing (HPC)Chapter Goal: Provide an introduction to token in Ethereum by highlighting applications in HPC. Particularly, focus on Golem, SONM, and iEx.ec grid computing for off-chain computations and conflict resolution.Sub -Topics:1. Why tokens and what’s the value of using tokens2. Introduction to tokens, ERC 20 compatibility3. Token layer and an app layer4. Prototype for tokens and HPC – Ethereum Computation Markets5. Golem network, app registry, transaction framework, use-cases initially, and how the Smart Contract system ties them together6. SONM network and fog computing, use-cases, Smart Contract system, buyer-miner-hub interactions, purchasing computational power, Superglobal architecture, and OS7. iEx.ec, grid computing, sidechains, and how iEx.ec works Chapter 8: Blockchain in HealthcareChapter Goal: Provide an introduction to areas in healthcare where using a blockchain can provide benefit - Patient workflows, insurance claims processing, lightning network, verifiable data auditSub -Topics:1. Payer-provider-patient model, how claims work within this framework, and how will that change in the future2. Patient workflow based on permissions, blockchain-based workflow of a simple EHR, how permissions are passed as a patient moves from a general physician to a specialist 3. Show how permissions work in blockchain insurance claims processing4. Waste management in healthcare and claims processing5. Concept of hotswitching, mentioning lightning network6. How can blockchain be used to reduce economic waste 7. DeepMind’s Verifiable Data Audit as an alternative to blockchain8. Blockchain to streamline business processesChapter 9: Blockchain in ScienceChapter Goal: Provide an introduction to major topics in science where blockchain can be beneficial – Reputation markets, reproducibility crisis, drug tracking, digital clinical trialsSub -Topics:1. Reproducibility crisis in science2. Prediction markets in science – Augur and Gnosis3. Initiatives to fix reproducibility traditionally4. Clinical trials using the blockchain, colored coins to demonstrate workflow5. Reputation systems using Blockchain6. Pharma drug tracking using blockchainChapter 10: Building Healthcare Companies on BlockchainChapter Goal: Interview with John Bass on how to build a healthcare company on the blockchain and lessons learned along the way.Sub -Topics:1. The makings of Hashed Health2. Collaborative and consortium models3. Working groups for high-risk, high-reward technologies4. Governance models for Hashed Health consortia5. Member participationChapter 11: Rise of ConsortiumsChapter Goal: Provide an overview of consortium models that have become popular in the blockchain industry, the challenges consortiums hope to solve, and advantages to individual membersSub -Topics:1. Collaborative and consortium models2. Working groups for high-risk, high-reward technologies3. Governance models for Hashed Health consortiaChapter 12: The Hyperledger ProjectChapter Goal: Provide a broad overview of the Hyperledger Project and cover the rapid pace of developments since 2018 to the new products launched.Sub -Topics:1. Updates to all the components under Hyperledger umbrella including Fabric and Sawtooth2. New consensus algorithm (PBFT)3. Demo of Hyperledger Fabric Constructor4. Does your business need a blockchain (flowcharts)?5. Security in enterprise-grade Blockchains6. Smart Contracts in FabricChapter 13: Recent Advances in BlockchainChapter Goal: Provide a review of three major networks shaping the future of Blockchain – EOS.io with parallel processing virtual machine, chain-core with asset management, and Ivy Playground and Quorum with private–public transaction interfacesSub -Topics:1. EOS.io, how the tech works, the new advances such as parallel processing of smart contracts and instructions2. Chain Core, managing assets on blockchain, Ivy Playground as the new programming language to manage assets3. Quorum by JP Morgan, how the consensus works, how private transactions work, zero-knowledge proofs, Ethereum Enterprise AllianceChapter 14: Blockchain GamesChapter Goal: Provide a review of the educational games and APIs that have been released to teach the basic concepts of a blockchain networkSub -Topics:1. Components of a blockchain game2. Formal education and training in blockchain3. Formalization of blockchain study with journals and research articles4. Review of major blockchain games (3)Chapter 15: Cloud BlockchainsChapter Goal: Provide an overview of how to click and deploy a blockchain using cloud services and give a visual tutorial on how to set it upSub -Topics:1. Demo of Hyperledger Fabric Constructor on IBM Bluemix 2. Azure blockchain deployment3. Amazon ECS cloud blockchain deployment4. Setting up your own blockchain test-lab and budget itChapter 16: Technological Revolutions and Financial CapitalChapter Goal: Provide an overview of the financial markets and ICOs, focusing particularly on how to set up ICOs, how to manage them, advances to financial regulation technology based on blockchainSub -Topics:1. Dr. Hooper’s chapter focused on ICOs and financial capital markets2. Setting up ICOs, major pitfalls to avoid, and challenges to overcome during an ICO3. Major tech advances in financial markets using blockchain4. Reg TechAfterword – Call to Action and The Future of Blockchain
£37.49
APress TensorFlow 2.x in the Colaboratory Cloud
Book SynopsisIntermediate-Advanced user levelTable of Contents1. Introduction to Deep Learning2. Build Your First Neural Network with Google Colab3. Working with TensorFlow Data4. Working with Other Data5. Classification6. Regression7. Convolutional Neural Networks8. Automated Text Generation9. Sentiment Analysis10. Time Series Forecasting with RNNs
£37.49
APress Foundation Db2 and Python
Book SynopsisThis module is not sponsored by IBM and must be installed separately from the Db2 database.After reading Foundation Db2 and Python you'll be able to install Db2 on Windows or Linux, and perform backups and restore data.Table of ContentsChapter 1: Introduction Chapter Goal: An introduction to Db2 for Linux and Windows.No. of Pages: 10Sub-Topics:Glossary of termsIntroduction to the Db2Obtaining the install files for Db2What you need as far as your hardware and OS for your Db2 machineHow to organize your file system to support Db2 data basesChapter 2: Installing Db2Chapter Goal: Describes how to install Db2 on Linux and WindowsNo. of Pages: 50Sub-Topics:Preparing your server for Db2Installing Db2 on LinuxInstalling Db2 on WindowsInstalling the ibm_db moduleTest the connection between Python/ibm_db and Db2Chapter 3: Db2 ManagementChapter Goal: Create Python scripts to access Db2No. of Pages: 40Sub-Topics:Layout of the typical Python script to access Db2The ibm_db moduleThe order of processing in almost all Python scripts accessing Db2Using exceptions in your Python scriptsChapter 4: Installing the Db2 sample database and a custom databaseChapter Goal: Learn how to design and create your own databasesNo. of Pages: 35Sub-Topics:Install the Db2 sample databaseWrite some simple Python scripts to access the sample data baseDesign and install the Orbital Launch data baseWrite some simple Python scripts to assess the Orbital Launch data baseChapter 5: Creating Utility Modules for Accessing Db2Chapter Goal: Creating modules that access ibm_dbNo. of Pages: 40Sub-Topics:Create you first utility moduleUse functions or classes in your module, or both?Organizing your module(s)Accessing your moduleChapter 6: Documenting the ibm_db ModuleChapter Goal: Documenting the ibm_db Module APINo. of Pages: 40Sub-Topics:Document each API in the ibm_db moduleDocument all input and outputs to each APIProvide multiple examples for each APIChapter 7: Writing Good SQL for Db2 (this might cover multiple chapters)Chapter Goal: Creating optimized SQL for Db2No. of Pages: 60Sub-Topics:Minimize the passes through the dataCode for concurrencyLocking and isolation levelsHow to avoid writing codeThe importance of indexesOptimizationSequential vs. random data accessTypes of joinsChapter 8: Where is the ibm_db Module GoingChapter Goal: Explain why IBM does not directly support the ibm_db module No. of Pages: 25Sub-Topics:Why ibm_db is open source?How does ibm_db use underlying systems?Why is this module not included with Db2?Some future items to be added to ibm_db moduleChapter 9: Db2 provided utilitiesChapter Goal: Describe some of the utilities that come with Db2 and possibly some extra cost utilitiesNo. of Pages: 50Subtopics:What utilities are covered has yet to be identifiedChapter 10: BLOB data, what is it and how do you use itChapter Goals: Describe what a blob is and how to use oneNo. of Pages: 50Subtopics:Describe the different kinds of BLOBsAccessing BLOB data in PythonWhat kinds of data can be stored in a BLOBUtilizing BLOBs to store Python data, how to keep data and metadata together in Db2
£41.24
APress R2DBC Revealed
Book SynopsisIntermediate user levelTable of ContentsIntroductionPart I. The Reactive Movement and R2DBC1. The Case for Reactive Programming2. Introduction to R2DBCPart II. The R2DBC Service-Provider Interface3. The Path to Implementation4. Connections5. Transactions6. Statements7. Handling Results8. Result Metadata9. Mapping Data Types10. Handling ExceptionsPart III. Getting Started with R2DBC and MariaDB11. Getting Stated with R2DBC12. Managing Connections13. Managing Data14. Managing Transactions15. Connection Pooling16. Practical Applications with Spring Data and R2DBC
£37.49
APress Modern Deep Learning for Tabular Data
Book SynopsisDeep learning is one of the most powerful tools in the modern artificial intelligence landscape. While having been predominantly applied to highly specialized image, text, and signal datasets, this book synthesizes and presents novel deep learning approaches to a seemingly unlikely domain - tabular data. Whether for finance, business, security, medicine, or countless other domain, deep learning can help mine and model complex patterns in tabular data - an incredibly ubiquitous form of structured data.Part I of the book offers a rigorous overview of machine learning principles, algorithms, and implementation skills relevant to holistically modeling and manipulating tabular data. Part II studies five dominant deep learning model designs - Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Attention and Transformers, and Tree-Rooted Networks - through both their ''default'' usage and their application to tabular data. Part III compounds the powTable of Contents○ Section 1: Machine Learning and Tabular Data ■ Chapter 1 – Introduction to Machine Learning ■ Chapter 2 – Data Tools ○ Section 2: Applied Deep Learning Architectures ■ Chapter 3 – Artificial Neural Networks ■ Chapter 4 – Convolutional Neural Networks ■ Chapter 5 – Recurrent Neural Networks ■ Chapter 6 – Attention Mechanism ■ Chapter 7 – Tree-based Neural Networks ○ Section 3: Deep Learning Design and Tools ■ Chapter 8 – Autoencoders ■ Chapter 9 – Data Generation ■ Chapter 10 – Meta-optimization ■ Chapter 11 – Multi-model arrangement ■ Chapter 12 – Deep Learning Interpretability ○ Appendix A
£41.24
APress Numerical Methods Using Kotlin
Book SynopsisIntermediate-Advanced user levelTable of Contents1: Introduction to Numerical Methods in Kotlin.- 2: Linear Algebra.- 3: Finding Roots of Equations.-4: Finding Roots of Systems of Equations.- 5: Curve Fitting and Interpolation.- 6: Numerical Differentiation and Integration.- 7: Ordinary Differential Equations.- 8: Partial Differential Equations.- 9: Unconstrained Optimization.- 10: Constrained Optimization.- 11: Heuristics.- 12: Basic Statistics.- 13: Random Numbers and Simulation.- 14: Linear Regression.- 15: Time Series Analysis.- References.Table of ContentsAbout the Authors...........................................................................................................iPreface............................................................................................................................ii1. Why Kotlin?..............................................................................................................61.1. Kotlin in 2022.....................................................................................................61.2. Kotlin vs. C++....................................................................................................61.3. Kotlin vs. Python................................................................................................61.4. Kotlin in the future .............................................................................................62. Data Structures.......................................................................................................72.1. Function...........................................................................................................72.2. Polynomial ......................................................................................................73. Linear Algebra .......................................................................................................83.1. Vector and Matrix ...........................................................................................83.1.1. Vector Properties .....................................................................................83.1.2. Element-wise Operations.........................................................................83.1.3. Norm ........................................................................................................93.1.4. Inner product and angle ...........................................................................93.2. Matrix............................................................................................................103.3. Determinant, Transpose and Inverse.............................................................103.4. Diagonal Matrices and Diagonal of a Matrix................................................103.5. Eigenvalues and Eigenvectors.......................................................................103.5.1. Householder Tridiagonalization and QR Factorization Methods..........103.5.2. Transformation to Hessenberg Form (Nonsymmetric Matrices)...........104. Finding Roots of Single Variable Equations .......................................................114.1. Bracketing Methods ......................................................................................114.1.1. Bisection Method ...................................................................................114.2. Open Methods...............................................................................................114.2.1. Fixed-Point Method ...............................................................................114.2.2. Newton’s Method (Newton-Raphson Method) .....................................114.2.3. Secant Method .......................................................................................114.2.4. Brent’s Method ......................................................................................115. Finding Roots of Systems of Equations...............................................................125.1. Linear Systems of Equations.........................................................................125.2. Gauss Elimination Method............................................................................125.3. LU Factorization Methods ............................................................................125.3.1. Cholesky Factorization ..........................................................................125.4. Iterative Solution of Linear Systems.............................................................125.5. System of Nonlinear Equations.....................................................................126. Curve Fitting and Interpolation............................................................................146.1. Least-Squares Regression .............................................................................146.2. Linear Regression..........................................................................................146.3. Polynomial Regression..................................................................................146.4. Polynomial Interpolation...............................................................................146.5. Spline Interpolation .......................................................................................147. Numerical Differentiation and Integration...........................................................157.1. Numerical Differentiation .............................................................................157.2. Finite-Difference Formulas...........................................................................157.3. Newton-Cotes Formulas................................................................................157.3.1. Rectangular Rule....................................................................................157.3.2. Trapezoidal Rule....................................................................................157.3.3. Simpson’s Rules.....................................................................................157.3.4. Higher-Order Newton-Coles Formulas..................................................157.4. Romberg Integration .....................................................................................157.4.1. Gaussian Quadrature..............................................................................157.4.2. Improper Integrals..................................................................................158. Numerical Solution of Initial-Value Problems....................................................168.1. One-Step Methods.........................................................................................168.2. Euler’s Method..............................................................................................168.3. Runge-Kutta Methods...................................................................................168.4. Systems of Ordinary Differential Equations.................................................169. Numerical Solution of Partial Differential Equations..........................................179.1. Elliptic Partial Differential Equations...........................................................179.1.1. Dirichlet Problem...................................................................................179.2. Parabolic Partial Differential Equations........................................................179.2.1. Finite-Difference Method ......................................................................179.2.2. Crank-Nicolson Method.........................................................................179.3. Hyperbolic Partial Differential Equations.....................................................1710..................................................................................................................................1811..................................................................................................................................1912. Random Numbers and Simulation ....................................................................2012.1. Uniform Distribution .................................................................................2012.2. Normal Distribution...................................................................................2012.3. Exponential Distribution............................................................................2012.4. Poisson Distribution ..................................................................................2012.5. Beta Distribution........................................................................................2012.6. Gamma Distribution ..................................................................................2012.7. Multi-dimension Distribution ....................................................................2013. Unconstrainted Optimization ............................................................................2113.1. Single Variable Optimization ....................................................................2113.2. Multi Variable Optimization .....................................................................2114. Constrained Optimization .................................................................................2214.1. Linear Programming..................................................................................2214.2. Quadratic Programming ............................................................................2214.3. Second Order Conic Programming............................................................2214.4. Sequential Quadratic Programming...........................................................2214.5. Integer Programming.................................................................................2215. Heuristic Optimization......................................................................................2315.1. Genetic Algorithm .....................................................................................2315.2. Simulated Annealing .................................................................................2316. Basic Statistics..................................................................................................2416.1. Mean, Variance and Covariance................................................................2416.2. Moment......................................................................................................2416.3. Rank...........................................................................................................2417. Linear Regression .............................................................................................2517.1. Least-Squares Regression..........................................................................2517.2. General Linear Least Squares....................................................................2518. Time Series Analysis ........................................................................................2618.1. Univariate Time Series..............................................................................2618.2. Multivariate Time Series ...........................................................................2618.3. ARMA .......................................................................................................2618.4. GARCH .....................................................................................................2618.5. Cointegration .............................................................................................2619. Bibliography .....................................................................................................2720. Index .....................................................................................................
£41.24
APress Architecture of Advanced Numerical Analysis
Book SynopsisThis unique open access book applies the functional OCaml programming language to numerical or computational weighted data science, engineering, and scientific applications. This book is based on the authors' first-hand experience building and maintaining Owl, an OCaml-based numerical computing library.You'll first learn the various components in a modern numerical computation library. Then, you will learn how these components are designed and built up and how to optimize their performance. After reading and using this book, you'll have the knowledge required to design and build real-world complex systems that effectively leverage the advantages of the OCaml functional programming language. What You Will LearnOptimize core operations based on N-dimensional arraysDesign and implement an industry-level algorithmic differentiation moduleImplement mathematical optimization, regression, and deep neural network functionalities based on algorithmic differentiationDesign and optimize a compTable of ContentsPrologueA Brief HistoryReductionism vs. HolismKey FeaturesContact MePART 1: NUMERICAL TECHNIQUES1. IntroductionWhat Is Scientific ComputingWhat is Functional ProgrammingWho Is This Book ForStructure of the BookInstallationOption 1: Install from OPAMOption 2: Pull from Docker HubOption 3: Pin the Dev-RepoOption 4: Compile from SourceCBLAS/LAPACKE DependencyInteracting with OwlUsing ToplevelUsing NotebookUsing Owl-JupyterSummary2. ConventionsPure vs. ImpureNdarray vs. ScalarInfix OperatorsOperator ExtensionModule StructuresNumber and PrecisionPolymorphic FunctionsModule ShortcutsType Casting3. VisualisationCreate PlotsSpecificationSubplotsMultiple LinesLegendDrawing PatternsLine PlotScatter PlotStairs PlotBox PlotStem PlotArea PlotHistogram & CDF PlotLog Plot3D PlotAdvanced Statistical PlotSummaryReferences4. Mathematical FunctionsBasic FunctionsBasic Unary Math FunctionsBasic Binary FunctionsExponential and Logarithmic FunctionsTrigonometric FunctionsOther Math FunctionsSpecial FunctionsAiry FunctionsBessel FunctionsElliptic FunctionsGamma FunctionsBeta FunctionsStruve FunctionsZeta FunctionsError FunctionsIntegral FunctionsFactorialsInterpolation and ExtrapolationIntegrationUtility FunctionsSummary5. Statistical FunctionsRandom VariablesDiscrete Random VariablesContinuous Random VariablesDescriptive StatisticsOrder StatisticsSpecial DistributionGamma DistributionBeta DistributionChi-Square DistributionStudent-t DistributionCauchy DistributionMultiple VariablesSamplingHypothesis TestsTheoryGaussian Distribution in Hypothesis TestingTwo-Sample InferencesGoodness-of-fit TestsNon-parametric StatisticsCovariance and CorrelationsAnalysis of VarianceSummary6. N-Dimensional ArraysNdarray TypesCreation FunctionsProperties FunctionsMap FunctionsFold FunctionsScan FunctionsComparison FunctionsVectorised FunctionsIteration FunctionsManipulation FunctionsSerialisationTensorsSummaryReferences7. Slicing and BroadcastingSlicingBasic SlicingFancy SlicingConventions in DefinitionExtended OperatorsAdvanced UsageBroadcastingWhat Is Broadcasting?Shape ConstraintsSupported OperationsSlicing in NumPy and JuliaInternal MechanismSummary8. Linear AlgebraVectors and MatricesCreating MatricesAccessing ElementsIterate, Map, Fold, and FilterMath OperationsGaussian EliminationLU FactorisationInverse and TransposeVector SpacesRank and BasisOrthogonalitySolving Ax = bMatrix SensitivityDeterminantsEigenvalues and EigenvectorsSolving Ax=λ xComplex MatricesSimilarity Transformation and DiagonalisationPositive Definite MatricesPositive DefinitenessSingular Value DecompositionInternal: CBLAS and LAPACKELow-level Interface to CBLAS & LAPACKESparse MatricesSummaryReferences9. Ordinary Differential EquationsWhat Is An ODEExact SolutionsLinear SystemsSolving An ODE NumericallyOwl-ODEExample: Linear Oscillator SystemSolver StructureSymplectic SolversFeatures and LimitsExamples of using Owl-ODEExplicit ODETwo Body ProblemLorenz AttractorDamped OscillationStiffnessSolve Non-Stiff ODEsSolve Stiff ODEsSummaryReferences10. Signal ProcessingDiscrete Fourier TransformFast Fourier TransformExamplesApplications of FFTFind period of sunspotsDecipher the ToneImage ProcessingFilteringExample: SmoothingGaussian FilterSignal ConvolutionFFT and Image ConvolutionSummaryReferences11. Algorithmic DifferentiationChain RuleDifferentiation MethodsHow Algorithmic Differentiation WorksForward ModeReverse ModeForward or Reverse?A Strawman AD EngineSimple Forward ImplementationSimple Reverse ImplementationUnified ImplementationsForward and Reverse Propagation APIExpressing ComputationExample: Forward ModeExample: Reverse ModeHigh-Level APIsDerivative and GradientJacobianHessian and LaplacianOther APIsInternal of Algorithmic DifferentiationGo Beyond Simple ImplementationExtend AD moduleLazy EvaluationSummaryReferences12. OptimisationIntroductionRoot FindingUnivariate Function OptimisationUse DerivativesGolden Section SearchMultivariate Function OptimisationNelder-Mead Simplex MethodGradient Descent MethodsConjugate Gradient MethodNewton and Quasi-Newton MethodsGlobal Optimisation and Constrained OptimisationSummaryReferences13. RegressionLinear RegressionProblem: Where to locate a new McDonald’s restaurant?Cost FunctionSolving Problem with Gradient DescentMultiple RegressionFeature NormalisationAnalytical SolutionNon-linear regressionsRegularisationOls, Ridge, Lasso, and Elastic_netLogistic RegressionSigmoid FunctionCost FunctionExampleMulti-class classificationSupport Vector MachineKernel and Non-linear BoundaryExampleModel error and selectionError MetricsModel SelectionSummaryReferences14. Deep Neural NetworksPerceptronYet Another RegressionModel RepresentationForward PropagationBack propagationFeed Forward NetworkLayersActivation FunctionsInitialisationTrainingTestNeural Network ModuleModule StructureNeuronsNeural GraphTraining ParametersConvolutional Neural NetworkRecurrent Neural NetworkLong Short Term Memory (LSTM)Generative Adversarial NetworkSummaryReferences15. Natural Language ProcessingIntroductionText CorpusStep-by-step OperationUse the Corpus ModuleVector Space ModelsBag of Words (BOW)Term Frequency–Inverse Document Frequency (TF-IDF)Latent Dirichlet Allocation (LDA)ModelsDirichlet DistributionGibbs SamplingTopic Modelling ExampleLatent Semantic Analysis (LSA)Search Relevant DocumentsEuclidean and Cosine SimilarityLinear SearchingSummaryReferences16. Dataframe for Tabular DataBasic ConceptsCreate FramesManipulate FramesQuery FramesIterate, Map, and FilterRead/Write CSV FilesInfer Type and SeparatorSummary17. Symbolic RepresentationIntroductionDesignCore abstractionEnginesONNX EngineExample 1: Basic operationsExample 2: Variable InitialisationExample 3: Neural networkLaTeX EngineOwl EngineSummary18. Probabilistic ProgrammingGenerative Model vs Discriminative ModelBayesian NetworksSampling TechniquesInferencePART 2: SYSTEM ARCHITECTURE19. Architecture OverviewIntroductionArchitecture OverviewCore ImplementationN-dimensional ArrayInterfaced LibrariesAdvanced FunctionalityComputation GraphAlgorithmic DifferentiationRegressionNeural NetworkParallel ComputingActor EngineGPU ComputingOpenMPCommunity-Driven R&DSummary20. Core OptimisationBackgroundNumerical LibrariesOptimisation of Numerical ComputationInterfacing to C CodeNdarray OperationsFrom OCaml to COptimisation TechniquesMap OperationsConvolution OperationsReduction OperationsRepeat OperationsSummaryReferences21. Automatic Empirical TuningWhat is Parameter TuningWhy Parameter Tuning in OwlHow to Tune OpenMP ParametersMake a DifferenceSummary22. Computation GraphIntroductionWhat is a Computation Graph?From Dynamic to StaticSignificance in ComputingExamplesExample 01: Basic CGraphExample 02: CGraph with ADExample 03: CGraph with DNNDesign RationaleOptimisation of CGraphOptimising memory with pebblesAllocation AlgorithmAs Intermediate RepresentationsSummary23. Scripting and Zoo SystemIntroductionShare Script with ZooTypical ScenarioCreate a ScriptShare via GistImport in Another ScriptSelect a Specific VersionCommand Line ToolMore ExamplesSystem DesignServicesType CheckingBackendDomain Specific LanguageService DiscoveryUse CaseSummaryReferences24. Compiler BackendsBase LibraryBackend: JavaScriptUse Native OCamlUse Facebook ReasonBackend: MirageOSMirageOS and UnikernelExample: Gradient DescentExample: Neural NetworkEvaluationSummary25. Distributed ComputingActor SystemDesignActor EnginesMap-Reduce EngineParameter Server EnginePeer-to-Peer EngineClassic Synchronise ParallelBulk Synchronous ParallelAsynchronous ParallelStale Synchronous ParallelProbabilistic Synchronise ParallelBasic idea: samplingCompatibilityBarrier Trade-off DimensionsConvergenceA Distributed Training ExampleStep ProgressAccuracySummaryReferences26. Testing FrameworkUnit TestExampleWhat Could Go WrongCorner CasesTest CoverageUse FunctorSummary27. Constants and Metric SystemWhat Is a Metric SystemFour Metric SystemsSI PrefixExample: Physics and Math constantsInternational System of UnitsTimeLengthAreaVolumeSpeedMassForceEnergyPowerPressureViscosityLuminanceRadioactivity28. Internal Utility ModulesDataset ModuleMNISTCIFAR-10Graph ModuleStack and Heap ModulesCount-Min SketchSummaryPART 3: CASE STUDIES29. Case - Image RecognitionBackgroundLeNetAlexNetVGGResNetSqueezeNetCapsule NetworkBuilding InceptionV3 NetworkInceptionV1 and InceptionV2FactorisationGrid Size ReductionInceptionV3 ArchitecturePreparing WeightsProcessing ImageRunning InferenceApplicationsSummaryReferences30. Case - Instance SegmentationIntroductionMask R-CNN NetworkBuilding Mask R-CNNFeature ExtractorProposal GenerationClassificationRun the CodeSummaryReferences31. Case - Neural Style TransferContent and StyleContent ReconstructionStyle RecreationCombining Content and StyleRunning NSTExtending NSTFast Style TransferBuilding FST NetworkRunning FSTSummaryReferences32. Case - Recommender SystemIntroductionArchitectureBuild Topic ModelsIndex Text CorpusRandom ProjectionOptimising Vector StorageOptimise Data StructureOptimise Index AlgorithmSearch ArticlesCode ImplementationMake It LiveSummaryReferences33. Case - Applications in FinanceIntroductionBond PricingBlack-Scholes ModelMathematical ModelOption PricingPortfolio OptimisationMathematical ModelEfficient FrontierMaximise Sharpe Ratio
£33.74
APress PyTorch Recipes
Book SynopsisLearn how to use PyTorch to build neural network models using code snippets updated for this second edition. This book includes new chapters covering topics such as distributed PyTorch modeling, deploying PyTorch models in production, and developments around PyTorch with updated code. You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balaTrade Review“The book covers all important facets of neural network implementation and modeling, and could definitely be useful to students and developers keen for an in-depth look at how to build models using PyTorch, or how to engineer particular neural network features using this platform.” (Mariana Damova, Computing Reviews, July 24, 2023)Table of ContentsChapter 1: Introduction to PyTorch, Tensors, and Tensor OperationsChapter Goal: This chapter is to understand what is PyTorch and its basic building blocks.Chapter 2: Probability Distributions Using PyTorchChapter Goal: This chapter aims at covering different distributions compatible with PyTorch for data analysis. Chapter 3: Neural Networks Using PyTorchChapter Goal: This chapter explains the use of PyTorch to develop a neural network model and optimize the model.Chapter 4: Deep Learning (CNN and RNN) Using PyTorchChapter Goal: This chapter explains the use of PyTorch to train deep neural networks for complex datasets.Chapter 5: Language Modeling Using PyTorchChapter Goal: In this chapter, we are going to use torch text for natural language processing, pre-processing, and feature engineering. Chapter 6: Supervised Learning Using PyTorchGoal: This chapter explains how supervised learning algorithms implementation with PyTorch. Chapter 7: Fine Tuning Deep Learning Models using PyTorchGoal: This chapter explains how to Fine Tuning Deep Learning Models using the PyTorch framework.Chapter 8: Distributed PyTorch ModelingChapter Goal: This chapter explains the use of parallel processing using the PyTorch framework.Chapter 9: Model Optimization Using Quantization MethodsChapter Goal: This chapter explains the use of quantization methods to optimize the PyTorch models and hyperparameter tuning with ray tune. Chapter 10: Deploying PyTorch Models in ProductionChapter Goal: In this chapter we are going to use torch serve, to deploy the PyTorch models into production. Chapter 11: PyTorch for AudioChapter Goal: In this chapter torch audio will be used for audio resampling, data augmentation, features extractions, model training, and pipeline development. Chapter 12: PyTorch for ImageChapter Goal: This chapter aims at using Torchvision for image transformations, pre-processing, feature engineering, and model training. Chapter 13: Model Explainability using CaptumChapter Goal: In this chapter, we are going to use the captum library for model interpretability to explain the model as if you are explaining the model to a 5-year-old. Chapter 14: Scikit Learn Model compatibility using SkorchChapter Goal: In this chapter, we are going to use skorch which is a high-level library for PyTorch that provides full sci-kit learn compatibility.
£33.74
APress Make Your Data Speak
Book SynopsisTable of ContentsIntroduction. Three stories that made me write this bookChapter 1. Data preparation 1.1 Analyzing and transforming the original data 1.2 Preparing the basis for a dashboard 1.3 Making data samples for visualizations 1.4. Setting up an interactivity 1.5. Summary and conclusions of the chapter, quick tricks Chapter 2. Dashboard assembling 2.1 Assembling a dashboard according to the layout 2.2 Creating KPI cards 2.3 Aligning a dashboard, adding a header 2.4 Summary and conclusions of the chapter, quick tricks Chapter 3. Anatomy of diagrams 3.1 Analyzing ready-made design styles 3.2 Setting up data labels 3.3 Working with the text: remove the excess, add the necessary 3.4 Designing bar charts 3.5 Setting up the chart template 3.6 Summary and conclusions of the chapter, quick tricks Chapter 4. Final dashboard design 4.1 Aligning the headers to the grid 4.2 Creating new cards on the top of the cells 4.3 Making interactive slicers 4.4 Working with Excel colors and fonts 4.5 Improving standard Excel themes 4.6. Summary and conclusions of the chapter, quick tricks Chapter 5. Corporate identity 5.1 Creating a theme in accordance with the brandbook 5.2 Adapting the theme according to the checklist 5.3 Creating a dashboard in a dark theme 5.4 Summary and conclusions of the chapter, quick tricks Chapter 6. Data visualization rules 6.1 Types of data analysis 6.2 How to choose charts 6.3 Life hacks for multiple data series 6.4 When you need everything at once 6.5 Funnel and waterfall 6.6 Summary and conclusions of the chapter, quick tricks Conclusion
£41.24
APress MySQL Database Service Revealed
Book SynopsisIntermediate user levelTable of Contents1. Getting Started with MySQL in the Cloud2. Oracle Cloud Infrastructure3. A Brief Tutorial of MySQL4. MySQL Database Service5. Backup and Restore6. Point-in-Time Recovery7. Data Import and Export8. High Availability9. OCI Command-Line Interface and Application Programming Interfaces10. Migrating to MDS
£41.24
APress Oracle on Docker
Book SynopsisIntermediate user levelTable of ContentsIntroductionPart I. Introduction to Containers1. Introducing Docker and Oracle2. Understanding the Container Landscape3. Container Foundations4. Oracle Database Quick Start Guide5. Differences in Database Containers6. Customize Container Environments7. Persistence8. Basic Networking9. Container Networks10. Container Creation Quick ReferencePart II. Building and Customizing Images11. Customizing Images12. Dockerfile Syntax13. Dockerfiles for Orcale Databases14. Building Images15. Debugging and Troubleshooting16. Docker Hub and Image Repositories17. ConclusionPart III. AppendixesA. Installing Docker Desktop
£41.24
APress Blockchain for Hospitality and Tourism
Book SynopsisLearn blockchain in a simple, non-tech way and explore the different emerging technologies that open a world of opportunities in the space of tourism and hospitality. This book showcases examples of blockchain-based solutions implemented in different industries and connects them to use cases in hospitality and tourism (disintermediation, payments, loyalty programs, supply chain management, identity management etc.).Blockchain is one of the disruptive technologies that lays foundations for Web3.0, NFTs, Metaverse and other innovations. Despite many benefits, its adoption in the hospitality industry is very slow. Lack of awareness and connection to clear return-on-investment, coupled with many misconceptions and general perception of complexity is one of the main reasons why hospitality managers are reluctant to embark on the blockchain train. Blockchain for Hospitality and Tourism serves as a practical guide to the world of innovations, from the basics of blockchain to how to start a Table of ContentsChapter 1: Introduction Chapter goal: Intro to the topic of blockchain – why blockchain is a game-changer, what you’ll learn, why is it important to learn about emerging tech · Blockchain potential · Hospitality and Tourism challenges and trends and the correlation with new tech · Challenges with innovation adoption in the hospitality industry Chapter 2: Demystifying Blockchain Chapter goal: explain – in non-tech way with visualizations – what blockchain is and how it works · What is Blockchain · Blockchain characteristics · Smart Contracts · Blockchain ecosystem – platforms with capabilities · Foundational role of blockchain as an enabler for other innovation o Cryptopayments, stablecoins and CBDCs o NFTs o Web3.0 o Metaverse o Industrial Revolution 4.0 Chapter 3: Blockchain applications Chapter goal: explain how blockchain is utilized in different industries today with real-life examples · Banking & Insurance · Healthcare · Public sector/Government services · Supply chain management etc. Chapter 4: Use cases for Hospitality & Tourism Chapter goal: showcase solutions that have been implemented in different geographies, trends, and directions · Identity Management · Customer loyalty programs – NFTs and blockchain-based platforms · Smart contracts and supply chain management o Food security and provenance tracking o Preventive maintenance and Smart Hotel applications o Sustainability · New distribution methods and disintermediation · Payments acceptance · Guest preferences and personalization · Digitization of assets · NFTs · Metaverse opportunities – digital twins, virtual floor plan walkthroughs, virtual trainings, marketing etc. Chapter 5: Risks and Challenges Chapter goal: Discuss blockchain maturity and adoption, interoperability, and state of regulations; address concerns around trust, fraud etc. Chapter 6: Blockchain projects – how to start Chapter goal: a walkthrough of the most important steps and decisions
£29.99
SAP Press SAP HANA 2.0 Administration
Book SynopsisNovice or expert, this is your one-stop shop for administering SAP HANA 2.0! You’ll begin with a deep dive into database and engine architecture. Then explore your key tools: SAP HANA cockpit, SAP HANA Studio, the command-line interface, and more. From there, choose the topics you need.
£67.49
Microsoft Press,U.S. Exam Ref 70-764 Administering a SQL Database
Book SynopsisPrepare for Microsoft Exam 70-764—and help demonstrate your real-world mastery of skills for database administration. This exam is intended for database administrators charged with installation, maintenance, and configuration tasks. Their responsibilities also include setting up database systems, making sure those systems operate efficiently, and regularly storing, backing up, and securing data from unauthorized access. Focus on the expertise measured by these objectives: • Configure data access and auditing • Manage backup and restore of databases • Manage and monitor SQL Server instances • Manage high availability and disaster recovery This Microsoft Exam Ref: • Organizes its coverage by exam objectives • Features strategic, what-if scenarios to challenge you • Assumes you have working knowledge of database installation, configuration, and maintenance tasks. You should also have experience with setting up database systems, ensuring those systems operate efficiently, regularly storing and backing up data, and securing data from unauthorized access. About the Exam Exam 70-764 focuses on skills and knowledge required for database administration. About Microsoft Certification Passing both Exam 70-764 and Exam 70-765 (Provisioning SQL Databases) earns you credit toward an MCSA: SQL 2016 Database Administration certification. See full details at: microsoft.com/learning Table of Contents 1. Configure Data Access and Auditing 2. Manage Backup and Restore of Databases 3. Manage and Monitor SQL Server Instances 4. Manage High Availability and Disaster Recovery
£28.02
Pearson Education (US) CCNA Cybersecurity Operations Course Booklet
Book SynopsisYour Cisco Networking Academy Course Booklet is designed as a study resource you can easily read, highlight, and review on the go, wherever the Internet is not available or practical: · The text is extracted directly, word-for-word, from the online course so you can highlight important points and take notes in the “Your Chapter Notes” section. · Headings with the exact page correlations provide a quick reference to the online course for your classroom discussions and exam preparation. · An icon system directs you to the online curriculum to take full advantage of the images embedded within the Networking Academy online course interface and reminds you to perform the labs, Class Activities, interactive activities, Packet Tracer activities, watch videos, and take the chapter quizzes and exams. The Course Booklet is a basic, economical paper-based resource to help you succeed with the Cisco Networking Academy online course. Table of ContentsChapter 0 Course Introduction 1 0.0 Welcome to CCNA: Cybersecurity Operations 1 0.0.1 Message to the Student 1 Chapter 1 Cybersecurity and the Security Operations Center 5 1.0 Introduction 5 1.1 The Danger 5 1.1.1 War Stories 5 1.1.1.1 Hijacked People 5 1.1.1.2 Ransomed Companies 5 1.1.1.3 Targeted Nations 6 1.1.1.4 Lab - Installing the CyberOps Workstation Virtual Machine 6 1.1.1.5 Lab - Cybersecurity Case Studies 6 1.1.2 Threat Actors 6 1.1.2.1 Amateurs 6 1.1.2.2 Hacktivists 7 1.1.2.3 Financial Gain 7 1.1.2.4 Trade Secrets and Global Politics 7 1.1.2.5 How Secure is the Internet of Things? 7 1.1.2.6 Lab - Learning the Details of Attacks 7 1.1.3 Threat Impact 8 1.1.3.1 PII and PHI 8 1.1.3.2 Lost Competitive Advantage 8 1.1.3.3 Politics and National Security 8 1.1.3.4 Lab - Visualizing the Black Hats 9 1.2 Fighters in the War Against Cybercrime 9 1.2.1 The Modern Security Operations Center 9 1.2.1.1 Elements of a SOC 9 1.2.1.2 People in the SOC 9 1.2.1.3 Process in the SOC 10 1.2.1.4 Technologies in the SOC 10 1.2.1.5 Enterprise and Managed Security 10 1.2.1.6 Security vs. Availability 11 1.2.1.7 Activity - Identify the SOC Terminology 11 1.2.2 Becoming a Defender 11 1.2.2.1 Certifications 11 1.2.2.2 Further Education 12 1.2.2.3 Sources of Career Information 12 1.2.2.4 Getting Experience 13 1.2.2.5 Lab - Becoming a Defender 13 1.3 Summary 13 Chapter 2 Windows Operating System 17 2.0 Introduction 17 2.1 Windows Overview 17 2.1.1 Windows History 17 2.1.1.1 Disk Operating System 17 2.1.1.2 Windows Versions 18 2.1.1.3 Windows GUI 19 2.1.1.4 Operating System Vulnerabilities 19 2.1.2 Windows Architecture and Operations 20 2.1.2.1 Hardware Abstraction Layer 20 2.1.2.2 User Mode and Kernel Mode 21 2.1.2.3 Windows File Systems 21 2.1.2.4 Windows Boot Process 23 2.1.2.5 Windows Startup and Shutdown 24 2.1.2.6 Processes, Threads, and Services 25 2.1.2.7 Memory Allocation and Handles 25 2.1.2.8 The Windows Registry 26 2.1.2.9 Activity - Identify the Windows Registry Hive 27 2.1.2.10 Lab - Exploring Processes, Threads, Handles, and Windows Registry 27 2.2 Windows Administration 27 2.2.1 Windows Configuration and Monitoring 27 2.2.1.1 Run as Administrator 27 2.2.1.2 Local Users and Domains 27 2.2.1.3 CLI and PowerShell 28 2.2.1.4 Windows Management Instrumentation 29 2.2.1.5 The net Command 30 2.2.1.6 Task Manager and Resource Monitor 30 2.2.1.7 Networking 31 2.2.1.8 Accessing Network Resources 33 2.2.1.9 Windows Server 33 2.2.1.10 Lab - Create User Accounts 34 2.2.1.11 Lab - Using Windows PowerShell 34 2.2.1.12 Lab - Windows Task Manager 34 2.2.1.13 Lab - Monitor and Manage System Resources in Windows 34 2.2.2 Windows Security 34 2.2.2.1 The netstat Command 34 2.2.2.2 Event Viewer 35 2.2.2.3 Windows Update Management 35 2.2.2.4 Local Security Policy 35 2.2.2.5 Windows Defender 36 2.2.2.6 Windows Firewall 37 2.2.2.7 Activity - Identify the Windows Command 37 2.2.2.8 Activity - Identify the Windows Tool 37 2.3 Summary 37 Chapter 3 Linux Operating System 41 3.0 Introduction 41 3.1 Linux Overview 41 3.1.1 Linux Basics 41 3.1.1.1 What is Linux? 41 3.1.1.2 The Value of Linux 42 3.1.1.3 Linux in the SOC 42 3.1.1.4 Linux Tools 43 3.1.2 Working in the Linux Shell 43 3.1.2.1 The Linux Shell 43 3.1.2.2 Basic Commands 43 3.1.2.3 File and Directory Commands 44 3.1.2.4 Working with Text Files 44 3.1.2.5 The Importance of Text Files in Linux 44 3.1.2.6 Lab - Working with Text Files in the CLI 45 3.1.2.7 Lab - Getting Familiar with the Linux Shell 45 3.1.3 Linux Servers and Clients 45 3.1.3.1 An Introduction to Client-Server Communications 45 3.1.3.2 Servers, Services, and Their Ports 45 3.1.3.3 Clients 45 3.1.3.4 Lab - Linux Servers 45 3.2 Linux Administration 46 3.2.1 Basic Server Administration 46 3.2.1.1 Service Configuration Files 46 3.2.1.2 Hardening Devices 46 3.2.1.3 Monitoring Service Logs 47 3.2.1.4 Lab - Locating Log Files 48 3.2.2 The Linux File System 48 3.2.2.1 The File System Types in Linux 48 3.2.2.2 Linux Roles and File Permissions 49 3.2.2.3 Hard Links and Symbolic Links 50 3.2.2.4 Lab - Navigating the Linux Filesystem and Permission Settings 50 3.3 Linux Hosts 51 3.3.1 Working with the Linux GUI 51 3.3.1.1 X Window System 51 3.3.1.2 The Linux GUI 51 3.3.2 Working on a Linux Host 52 3.3.2.1 Installing and Running Applications on a Linux Host 52 3.3.2.2 Keeping the System Up To Date 52 3.3.2.3 Processes and Forks 52 3.3.2.4 Malware on a Linux Host 53 3.3.2.5 Rootkit Check 54 3.3.2.6 Piping Commands 54 3.3.2.7 Video Demonstration - Applications, Rootkits, and Piping Commands 55 3.4 Summary 55 Chapter 4 Network Protocols and Services 59 4.0 Introduction 59 4.1 Network Protocols 59 4.1.1 Network Communications Process 59 4.1.1.1 Views of the Network 59 4.1.1.2 Client-Server Communications 60 4.1.1.3 A Typical Session: Student 60 4.1.1.4 A Typical Session: Gamer 61 4.1.1.5 A Typical Session: Surgeon 61 4.1.1.6 Tracing the Path 62 4.1.1.7 Lab - Tracing a Route 62 4.1.2 Communications Protocols 62 4.1.2.1 What are Protocols? 62 4.1.2.2 Network Protocol Suites 63 4.1.2.3 The TCP/IP Protocol Suite 63 4.1.2.4 Format, Size, and Timing 64 4.1.2.5 Unicast, Multicast, and Broadcast 64 4.1.2.6 Reference Models 65 4.1.2.7 Three Addresses 65 4.1.2.8 Encapsulation 65 4.1.2.9 Scenario: Sending and Receiving a Web Page 66 4.1.2.10 Lab - Introduction to Wireshark 67 4.2 Ethernet and Internet Protocol (IP) 67 4.2.1 Ethernet 67 4.2.1.1 The Ethernet Protocol 67 4.2.1.2 The Ethernet Frame 68 4.2.1.3 MAC Address Format 68 4.2.1.4 Activity - Ethernet Frame Fields 68 4.2.2 IPv4 68 4.2.2.1 IPv4 Encapsulation 68 4.2.2.2 IPv4 Characteristics 69 4.2.2.3 Activity - IPv4 Characteristics 70 4.2.2.4 The IPv4 Packet 70 4.2.2.5 Video Demonstration - Sample IPv4 Headers in Wireshark 70 4.2.3 IPv4 Addressing Basics 70 4.2.3.1 IPv4 Address Notation 70 4.2.3.2 IPv4 Host Address Structure 70 4.2.3.3 IPv4 Subnet Mask and Network Address 71 4.2.3.4 Subnetting Broadcast Domains 71 4.2.3.5 Video Demonstration - Network, Host, and Broadcast Addresses 72 4.2.4 Types of IPv4 Addresses 72 4.2.4.1 IPv4 Address Classes and Default Subnet Masks 72 4.2.4.2 Reserved Private Addresses 73 4.2.5 The Default Gateway 73 4.2.5.1 Host Forwarding Decision 73 4.2.5.2 Default Gateway 74 4.2.5.3 Using the Default Gateway 74 4.2.6 IPv6 75 4.2.6.1 Need for IPv6 75 4.2.6.2 IPv6 Size and Representation 75 4.2.6.3 IPv6 Address Formatting 75 4.2.6.4 IPv6 Prefix Length 76 4.2.6.5 Activity - IPv6 Address Notation 76 4.2.6.6 Video Tutorial - Layer 2 and Layer 3 Addressing 76 4.3 Connectivity Verification 76 4.3.1 ICMP 76 4.3.1.1 ICMPv4 Messages 76 4.3.1.2 ICMPv6 RS and RA Messages 77 4.3.2 Ping and Traceroute Utilities 78 4.3.2.1 Ping - Testing the Local Stack 78 4.3.2.2 Ping - Testing Connectivity to the Local LAN 79 4.3.2.3 Ping - Testing Connectivity to Remote Host 79 4.3.2.4 Traceroute - Testing the Path 80 4.3.2.5 ICMP Packet Format 80 4.4 Address Resolution Protocol 81 4.4.1 MAC and IP 81 4.4.1.1 Destination on Same Network 81 4.4.1.2 Destination on Remote Network 82 4.4.2 ARP 82 4.4.2.1 Introduction to ARP 82 4.4.2.2 ARP Functions 82 4.4.2.3 Video - ARP Operation - ARP Request 83 4.4.2.4 Video - ARP Operation - ARP Reply 84 4.4.2.5 Video - ARP Role in Remote Communication 84 4.4.2.6 Removing Entries from an ARP Table 85 4.4.2.7 ARP Tables on Networking Devices 85 4.4.2.8 Lab - Using Wireshark to Examine Ethernet Frames 85 4.4.3 ARP Issues 85 4.4.3.1 ARP Broadcasts 85 4.4.3.2 ARP Spoofing 86 4.5 The Transport Layer 86 4.5.1 Transport Layer Characteristics 86 4.5.1.1 Transport Layer Protocol Role in Network Communication 86 4.5.1.2 Transport Layer Mechanisms 87 4.5.1.3 TCP Local and Remote Ports 87 4.5.1.4 Socket Pairs 88 4.5.1.5 TCP vs UDP 88 4.5.1.6 TCP and UDP Headers 89 4.5.1.7 Activity - Compare TCP and UDP Characteristics 90 4.5.2 Transport Layer Operation 90 4.5.2.1 TCP Port Allocation 90 4.5.2.2 A TCP Session Part I: Connection Establishment and Termination 91 4.5.2.3 Video Demonstration - TCP 3-Way Handshake 92 4.5.2.4 Lab - Using Wireshark to Observe the TCP 3-Way Handshake 92 4.5.2.5 Activity - TCP Connection and Termination Process 92 4.5.2.6 A TCP Session Part II: Data Transfer 92 4.5.2.7 Video Demonstration - Sequence Numbers and Acknowledgments 94 4.5.2.8 Video Demonstration - Data Loss and Retransmission 94 4.5.2.9 A UDP Session 94 4.5.2.10 Lab - Exploring Nmap 95 4.6 Network Services 95 4.6.1 DHCP 95 4.6.1.1 DHCP Overview 95 4.6.1.2 DHCPv4 Message Format 96 4.6.2 DNS 97 4.6.2.1 DNS Overview 97 4.6.2.2 The DNS Domain Hierarchy 97 4.6.2.3 The DNS Lookup Process 97 4.6.2.4 DNS Message Format 98 4.6.2.5 Dynamic DNS 99 4.6.2.6 The WHOIS Protocol 99 4.6.2.7 Lab - Using Wireshark to Examine a UDP DNS Capture 100 4.6.3 NAT 100 4.6.3.1 NAT Overview 100 4.6.3.2 NAT-Enabled Routers 100 4.6.3.3 Port Address Translation 100 4.6.4 File Transfer and Sharing Services 101 4.6.4.1 FTP and TFTP 101 4.6.4.2 SMB 102 4.6.4.3 Lab - Using Wireshark to Examine TCP and UDP Captures 102 4.6.5 Email 102 4.6.5.1 Email Overview 102 4.6.5.2 SMTP 102 4.6.5.3 POP3 103 4.6.5.4 IMAP 103 4.6.6 HTTP 103 4.6.6.1 HTTP Overview 103 4.6.6.2 The HTTP URL 104 4.6.6.3 The HTTP Protocol 104 4.6.6.4 HTTP Status Codes 105 4.6.6.5 Lab - Using Wireshark to Examine HTTP and HTTPS Traffic 105 4.7 Summary 105 Chapter 5 Network Infrastructure 109 5.0 Introduction 109 5.1 Network Communication Devices 109 5.1.1 Network Devices 109 5.1.1.1 End Devices 109 5.1.1.2 Video Tutorial - End Devices 109 5.1.1.3 Routers 110 5.1.1.4 Activity - Match Layer 2 and Layer 3 Addressing 110 5.1.1.5 Router Operation 110 5.1.1.6 Routing Information 111 5.1.1.7 Video Tutorial - Static and Dynamic Routing 112 5.1.1.8 Hubs, Bridges, LAN Switches 112 5.1.1.9 Switching Operation 113 5.1.1.10 Video Tutorial - MAC Address Tables on Connected Switches 114 5.1.1.11 VLANs 114 5.1.1.12 STP 114 5.1.1.13 Multilayer Switching 115 5.1.2 Wireless Communications 116 5.1.2.1 Video Tutorial - Wireless Communications 116 5.1.2.2 Protocols and Features 116 5.1.2.3 Wireless Network Operations 117 5.1.2.4 The Client to AP Association Process 118 5.1.2.5 Activity - Order the Steps in the Client and AP Association Process 119 5.1.2.6 Wireless Devices - AP, LWAP, WLC 119 5.1.2.7 Activity - Identify the LAN Device 119 5.2 Network Security Infrastructure 120 5.2.1 Security Devices 120 5.2.1.1 Video Tutorial - Security Devices 120 5.2.1.2 Firewalls 120 5.2.1.3 Firewall Type Descriptions 120 5.2.1.4 Packet Filtering Firewalls 121 5.2.1.5 Stateful Firewalls 121 5.2.1.6 Next-Generation Firewalls 121 5.2.1.7 Activity - Identify the Type of Firewall 122 5.2.1.8 Intrusion Protection and Detection Devices 122 5.2.1.9 Advantages and Disadvantages of IDS and IPS 122 5.2.1.10 Types of IPS 123 5.2.1.11 Specialized Security Appliances 124 5.2.1.12 Activity - Compare IDS and IPS Characteristics 125 5.2.2 Security Services 125 5.2.2.1 Video Tutorial - Security Services 125 5.2.2.2 Traffic Control with ACLs 125 5.2.2.3 ACLs: Important Features 126 5.2.2.4 Packet Tracer - ACL Demonstration 126 5.2.2.5 SNMP 126 5.2.2.6 NetFlow 127 5.2.2.7 Port Mirroring 127 5.2.2.8 Syslog Servers 128 5.2.2.9 NTP 128 5.2.2.10 AAA Servers 129 5.2.2.11 VPN 130 5.2.2.12 Activity - Identify the Network Security Device or Service 130 5.3 Network Representations 130 5.3.1 Network Topologies 130 5.3.1.1 Overview of Network Components 130 5.3.1.2 Physical and Logical Topologies 131 5.3.1.3 WAN Topologies 131 5.3.1.4 LAN Topologies 131 5.3.1.5 The Three-Layer Network Design Model 132 5.3.1.6 Video Tutorial - Three-Layer Network Design 132 5.3.1.7 Common Security Architectures 133 5.3.1.8 Activity - Identify the Network Topology 134 5.3.1.9 Activity - Identify the Network Design Terminology 134 5.3.1.10 Packet Tracer - Identify Packet Flow 134 5.4 Summary 134 Chapter 6 Principles of Network Security 137 6.0 Introduction 137 6.1 Attackers and Their Tools 137 6.1.1 Who is Attacking Our Network? 137 6.1.1.1 Threat, Vulnerability, and Risk 137 6.1.1.2 Hacker vs. Threat Actor 138 6.1.1.3 Evolution of Threat Actors 138 6.1.1.4 Cybercriminals 139 6.1.1.5 Cybersecurity Tasks 139 6.1.1.6 Cyber Threat Indicators 139 6.1.1.7 Activity - What Color is my Hat? 140 6.1.2 Threat Actor Tools 140 6.1.2.1 Introduction of Attack Tools 140 6.1.2.2 Evolution of Security Tools 140 6.1.2.3 Categories of Attacks 141 6.1.2.4 Activity - Classify Hacking Tools 141 6.2 Common Threats and Attacks 141 6.2.1 Malware 141 6.2.1.1 Types of Malware 141 6.2.1.2 Viruses 141 6.2.1.3 Trojan Horses 141 6.2.1.4 Trojan Horse Classification 142 6.2.1.5 Worms 142 6.2.1.6 Worm Components 143 6.2.1.7 Ransomware 143 6.2.1.8 Other Malware 144 6.2.1.9 Common Malware Behaviors 144 6.2.1.10 Activity - Identify the Malware Type 145 6.2.1.11 Lab - Anatomy of Malware 145 6.2.2 Common Network Attacks 145 6.2.2.1 Types of Network Attacks 145 6.2.2.2 Reconnaissance Attacks 145 6.2.2.3 Sample Reconnaissance Attacks 146 6.2.2.4 Access Attacks 146 6.2.2.5 Types of Access Attacks 147 6.2.2.6 Social Engineering Attacks 147 6.2.2.7 Phishing Social Engineering Attacks 148 6.2.2.8 Strengthening the Weakest Link 149 6.2.2.9 Lab - Social Engineering 149 6.2.2.10 Denial of Service Attacks 149 6.2.2.11 DDoS Attacks 149 6.2.2.12 Example DDoS Attack 150 6.2.2.13 Buffer Overflow Attack 150 6.2.2.14 Evasion Methods 151 6.2.2.15 Activity - Identify the Types of Network Attack 151 6.2.2.16 Activity - Components of a DDoS Attack 151 6.3 Summary 152 Chapter 7 Network Attacks: A Deeper Look 155 7.0 Introduction 155 7.1 Attackers and Their Tools 155 7.1.1 Who is Attacking Our Network? 155 7.1.1.1 Network Security Topology 155 7.1.1.2 Monitoring the Network 156 7.1.1.3 Network Taps 156 7.1.1.4 Traffic Mirroring and SPAN 156 7.1.2 Introduction to Network Monitoring Tools 157 7.1.2.1 Network Security Monitoring Tools 157 7.1.2.2 Network Protocol Analyzers 157 7.1.2.3 NetFlow 158 7.1.2.4 SIEM 159 7.1.2.5 SIEM Systems 159 7.1.2.6 Activity - Identify the Network Monitoring Tool 159 7.1.2.7 Packet Tracer - Logging Network Activity 159 7.2 Attacking the Foundation 160 7.2.1 IP Vulnerabilities and Threats 160 7.2.1.1 IPv4 and IPv6 160 7.2.1.2 The IPv4 Packet Header 160 7.2.1.3 The IPv6 Packet Header 161 7.2.1.4 IP Vulnerabilities 161 7.2.1.5 ICMP Attacks 162 7.2.1.6 DoS Attacks 163 7.2.1.7 Amplification and Reflection Attacks 163 7.2.1.8 DDoS Attacks 163 7.2.1.9 Address Spoofing Attacks 164 7.2.1.10 Activity - Identify the IP Vulnerability 164 7.2.1.11 Lab - Observing a DDoS Attack 164 7.2.2 TCP and UDP Vulnerabilities 165 7.2.2.1 TCP 165 7.2.2.2 TCP Attacks 165 7.2.2.3 UDP and UDP Attacks 166 7.2.2.4 Lab - Observing TCP Anomalies 166 7.3 Attacking What We Do 167 7.3.1 IP Services 167 7.3.1.1 ARP Vulnerabilities 167 7.3.1.2 ARP Cache Poisoning 167 7.3.1.3 DNS Attacks 168 7.3.1.4 DNS Tunneling 169 7.3.1.5 DHCP 169 7.3.1.6 Lab - Exploring DNS Traffic 170 7.3.2 Enterprise Services 170 7.3.2.1 HTTP and HTTPS 170 7.3.2.2 Email 173 7.3.2.3 Web-Exposed Databases 174 7.3.2.4 Lab - Attacking a MySQL Database 176 7.3.2.5 Lab - Reading Server Logs 176 7.3.2.6 Lab - Reading Server Logs 176 7.4 Summary 176 Chapter 8 Protecting the Network 179 8.0 Introduction 179 8.1 Understanding Defense 179 8.1.1 Defense-in-Depth 179 8.1.1.1 Assets, Vulnerabilities, Threats 179 8.1.1.2 Identify Assets 179 8.1.1.3 Identify Vulnerabilities 180 8.1.1.4 Identify Threats 181 8.1.1.5 Security Onion and Security Artichoke Approaches 181 8.1.2 Security Policies 182 8.1.2.1 Business Policies 182 8.1.2.2 Security Policy 182 8.1.2.3 BYOD Policies 183 8.1.2.4 Regulatory and Standard Compliance 184 8.2 Access Control 184 8.2.1 Access Control Concepts 184 8.2.1.1 Communications Security: CIA 184 8.2.1.2 Access Control Models 185 8.2.1.3 Activity - Identify the Access Control Model 185 8.2.2 AAA Usage and Operation 185 8.2.2.1 AAA Operation 185 8.2.2.2 AAA Authentication 186 8.2.2.3 AAA Accounting Logs 187 8.2.2.4 Activity - Identify the Characteristic of AAA 187 8.3 Threat Intelligence 187 8.3.1 Information Sources 187 8.3.1.1 Network Intelligence Communities 187 8.3.1.2 Cisco Cybersecurity Reports 188 8.3.1.3 Security Blogs and Podcasts 188 8.3.2 Threat Intelligence Services 188 8.3.2.1 Cisco Talos 188 8.3.2.2 FireEye 189 8.3.2.3 Automated Indicator Sharing 189 8.3.2.4 Common Vulnerabilities and Exposures Database 189 8.3.2.5 Threat Intelligence Communication Standards 189 8.3.2.6 Activity - Identify the Threat Intelligence Information Source 190 8.4 Summary 190 Chapter 9 Cryptography and the Public Key Infrastructure 193 9.0 Introduction 193 9.1 Cryptography 193 9.1.1 What is Cryptography? 193 9.1.1.1 Securing Communications 193 9.1.1.2 Cryptology 194 9.1.1.3 Cryptography - Ciphers 195 9.1.1.4 Cryptanalysis - Code Breaking 195 9.1.1.5 Keys 196 9.1.1.6 Lab - Encrypting and Decrypting Data Using OpenSSL 197 9.1.1.7 Lab - Encrypting and Decrypting Data Using a Hacker Tool 197 9.1.1.8 Lab - Examining Telnet and SSH in Wireshark 197 9.1.2 Integrity and Authenticity 197 9.1.2.1 Cryptographic Hash Functions 197 9.1.2.2 Cryptographic Hash Operation 198 9.1.2.3 MD5 and SHA 198 9.1.2.4 Hash Message Authentication Code 199 9.1.2.5 Lab - Hashing Things Out 200 9.1.3 Confidentiality 200 9.1.3.1 Encryption 200 9.1.3.2 Symmetric Encryption 200 9.1.3.3 Symmetric Encryption Algorithms 201 9.1.3.4 Asymmetric Encryption Algorithms 202 9.1.3.5 Asymmetric Encryption - Confidentiality 202 9.1.3.6 Asymmetric Encryption - Authentication 203 9.1.3.7 Asymmetric Encryption - Integrity 203 9.1.3.8 Diffie-Hellman 204 9.1.3.9 Activity - Classify the Encryption Algorithms 204 9.2 Public Key Infrastructure 204 9.2.1 Public Key Cryptography 204 9.2.1.1 Using Digital Signatures 204 9.2.1.2 Digital Signatures for Code Signing 206 9.2.1.3 Digital Signatures for Digital Certificates 206 9.2.1.4 Lab - Create a Linux Playground 206 9.2.2 Authorities and the PKI Trust System 206 9.2.2.1 Public Key Management 206 9.2.2.2 The Public Key Infrastructure 207 9.2.2.3 The PKI Authorities System 207 9.2.2.4 The PKI Trust System 208 9.2.2.5 Interoperability of Different PKI Vendors 208 9.2.2.6 Certificate Enrollment, Authentication, and Revocation 209 9.2.2.7 Lab - Certificate Authority Stores 209 9.2.3 Applications and Impacts of Cryptography 210 9.2.3.1 PKI Applications 210 9.2.3.2 Encrypting Network Transactions 210 9.2.3.3 Encryption and Security Monitoring 211 9.3 Summary 212 Chapter 10 Endpoint Security and Analysis 215 10.0 Introduction 215 10.1 Endpoint Protection 215 10.1.1 Antimalware Protection 215 10.1.1.1 Endpoint Threats 215 10.1.1.2 Endpoint Security 216 10.1.1.3 Host-Based Malware Protection 216 10.1.1.4 Network-Based Malware Protection 217 10.1.1.5 Cisco Advanced Malware Protection (AMP) 218 10.1.1.6 Activity - Identify Antimalware Terms and Concepts 218 10.1.2 Host-Based Intrusion Protection 218 10.1.2.1 Host-Based Firewalls 218 10.1.2.2 Host-Based Intrusion Detection 219 10.1.2.3 HIDS Operation 220 10.1.2.4 HIDS Products 220 10.1.2.5 Activity - Identify the Host-Based Intrusion Protection Terminology 220 10.1.3 Application Security 221 10.1.3.1 Attack Surface 221 10.1.3.2 Application Blacklisting and Whitelisting 221 10.1.3.3 System-Based Sandboxing 222 10.1.3.4 Video Demonstration - Using a Sandbox to Launch Malware 222 10.2 Endpoint Vulnerability Assessment 222 10.2.1 Network and Server Profiling 222 10.2.1.1 Network Profiling 222 10.2.1.2 Server Profiling 223 10.2.1.3 Network Anomaly Detection 223 10.2.1.4 Network Vulnerability Testing 224 10.2.1.5 Activity - Identify the Elements of Network Profiling 225 10.2.2 Common Vulnerability Scoring System (CVSS) 225 10.2.2.1 CVSS Overview 225 10.2.2.2 CVSS Metric Groups 225 10.2.2.3 CVSS Base Metric Group 226 10.2.2.4 The CVSS Process 226 10.2.2.5 CVSS Reports 227 10.2.2.6 Other Vulnerability Information Sources 227 10.2.2.7 Activity - Identify CVSS Metrics 228 10.2.3 Compliance Frameworks 228 10.2.3.1 Compliance Regulations 228 10.2.3.2 Overview of Regulatory Standards 228 10.2.3.3 Activity - Identify Regulatory Standards 229 10.2.4 Secure Device Management 230 10.2.4.1 Risk Management 230 10.2.4.2 Activity - Identify the Risk Response 231 10.2.4.3 Vulnerability Management 231 10.2.4.4 Asset Management 231 10.2.4.5 Mobile Device Management 232 10.2.4.6 Configuration Management 232 10.2.4.7 Enterprise Patch Management 233 10.2.4.8 Patch Management Techniques 233 10.2.4.9 Activity - Identify Device Management Activities 234 10.2.5 Information Security Management Systems 234 10.2.5.1 Security Management Systems 234 10.2.5.2 ISO-27001 234 10.2.5.3 NIST Cybersecurity Framework 234 10.2.5.4 Activity - Identify the ISO 27001 Activity Cycle 235 10.2.5.5 Activity - Identify the Stages in the NIST Cybersecurity Framework 235 10.3 Summary 235 Chapter 11 Security Monitoring 239 11.0 Introduction 239 11.1 Technologies and Protocols 239 11.1.1 Monitoring Common Protocols 239 11.1.1.1 Syslog and NTP 239 11.1.1.2 NTP 240 11.1.1.3 DNS 240 11.1.1.4 HTTP and HTTPS 241 11.1.1.5 Email Protocols 241 11.1.1.6 ICMP 242 11.1.1.7 Activity - Identify the Monitored Protocol 242 11.1.2 Security Technologies 242 11.1.2.1 ACLs 242 11.1.2.2 NAT and PAT 242 11.1.2.3 Encryption, Encapsulation, and Tunneling 243 11.1.2.4 Peer-to-Peer Networking and Tor 243 11.1.2.5 Load Balancing 244 11.1.2.6 Activity - Identify the Impact of the Technology on Security and Monitoring 244 11.2 Log Files 244 11.2.1 Types of Security Data 244 11.2.1.1 Alert Data 244 11.2.1.2 Session and Transaction Data 245 11.2.1.3 Full Packet Captures 245 11.2.1.4 Statistical Data 246 11.2.1.5 Activity - Identify Types of Network Monitoring Data 246 11.2.2 End Device Logs 246 11.2.2.1 Host Logs 246 11.2.2.2 Syslog 247 11.2.2.3 Server Logs 248 11.2.2.4 Apache Webserver Access Logs 248 11.2.2.5 IIS Access Logs 249 11.2.2.6 SIEM and Log Collection 249 11.2.2.7 Activity - Identify Information in Logged Events 250 11.2.3 Network Logs 250 11.2.3.1 Tcpdump 250 11.2.3.2 NetFlow 250 11.2.3.3 Application Visibility and Control 251 11.2.3.4 Content Filter Logs 251 11.2.3.5 Logging from Cisco Devices 252 11.2.3.6 Proxy Logs 252 11.2.3.7 NextGen IPS 253 11.2.3.8 Activity - Identify the Security Technology from the Data Description 254 11.2.3.9 Activity - Identify the NextGen IPS Event Type 254 11.2.3.10 Packet Tracer - Explore a NetFlow Implementation 254 11.2.3.11 Packet Tracer - Logging from Multiple Sources 254 11.3 Summary 254 Chapter 12 Intrusion Data Analysis 257 12.0 Introduction 257 12.1 Evaluating Alerts 257 12.1.1 Sources of Alerts 257 12.1.1.1 Security Onion 257 12.1.1.2 Detection Tools for Collecting Alert Data 257 12.1.1.3 Analysis Tools 258 12.1.1.4 Alert Generation 259 12.1.1.5 Rules and Alerts 260 12.1.1.6 Snort Rule Structure 260 12.1.1.7 Lab - Snort and Firewall Rules 261 12.1.2 Overview of Alert Evaluation 262 12.1.2.1 The Need for Alert Evaluation 262 12.1.2.2 Evaluating Alerts 262 12.1.2.3 Deterministic Analysis and Probabilistic Analysis 263 12.1.2.4 Activity - Identify Deterministic and Probabilistic Scenarios 264 12.1.2.5 Activity - Identify the Alert Classification 264 12.2 Working with Network Security Data 264 12.2.1 A Common Data Platform 264 12.2.1.1 ELSA 264 12.2.1.2 Data Reduction 264 12.2.1.3 Data Normalization 265 12.2.1.4 Data Archiving 265 12.2.1.5 Lab - Convert Data into a Universal Format 266 12.2.1.6 Investigating Process or API Calls 266 12.2.2 Investigating Network Data 266 12.2.2.1 Working in Sguil 266 12.2.2.2 Sguil Queries 267 12.2.2.3 Pivoting from Sguil 267 12.2.2.4 Event Handling in Sguil 268 12.2.2.5 Working in ELSA 268 12.2.2.6 Queries in ELSA 269 12.2.2.7 Investigating Process or API Calls 269 12.2.2.8 Investigating File Details 270 12.2.2.9 Lab - Regular Expression Tutorial 270 12.2.2.10 Lab - Extract an Executable from a PCAP 270 12.2.3 Enhancing the Work of the Cybersecurity Analyst 270 12.2.3.1 Dashboards and Visualizations 270 12.2.3.2 Workflow Management 271 12.3 Digital Forensics 271 12.3.1 Evidence Handling and Attack Attribution 271 12.3.1.1 Digital Forensics 271 12.3.1.2 The Digital Forensics Process 272 12.3.1.3 Types of Evidence 272 12.3.1.4 Evidence Collection Order 273 12.3.1.5 Chain of Custody 273 12.3.1.6 Data Integrity and Preservation 274 12.3.1.7 Attack Attribution 274 12.3.1.8 Activity - Identify the Type of Evidence 275 12.3.1.9 Activity - Identify the Forensic Technique Terminology 275 12.4 Summary 275 Chapter 13 Incident Response and Handling 277 13.0 Introduction 277 13.1 Incident Response Models 277 13.1.1 The Cyber Kill Chain 277 13.1.1.1 Steps of the Cyber Kill Chain 277 13.1.1.2 Reconnaissance 278 13.1.1.3 Weaponization 278 13.1.1.4 Delivery 278 13.1.1.5 Exploitation 279 13.1.1.6 Installation 279 13.1.1.7 Command and Control 279 13.1.1.8 Actions on Objectives 279 13.1.1.9 Activity - Identify the Kill Chain Step 279 13.1.2 The Diamond Model of Intrusion 280 13.1.2.1 Diamond Model Overview 280 13.1.2.2 Pivoting Across the Diamond Model 280 13.1.2.3 The Diamond Model and the Cyber Kill Chain 281 13.1.2.4 Activity - Identify the Diamond Model Features 282 13.1.3 The VERIS Schema 282 13.1.3.1 What is the VERIS Schema? 282 13.1.3.2 Create a VERIS Record 282 13.1.3.3 Top-Level and Second-Level Elements 283 13.1.3.4 The VERIS Community Database 285 13.1.3.5 Activity - Apply the VERIS Schema to an Incident 285 13.2 Incident Handling 285 13.2.1 CSIRTs 285 13.2.1.1 CSIRT Overview 285 13.2.1.2 Types of CSIRTs 286 13.2.1.3 CERT 286 13.2.1.4 Activity - Match the CSIRT with the CSIRT Goal 287 13.2.2 NIST 800-61r2 287 13.2.2.1 Establishing an Incident Response Capability 287 13.2.2.2 Incident Response Stakeholders 288 13.2.2.3 NIST Incident Response Life Cycle 288 13.2.2.4 Preparation 289 13.2.2.5 Detection and Analysis 290 13.2.2.6 Containment, Eradication, and Recovery 291 13.2.2.7 Post-Incident Activities 293 13.2.2.8 Incident Data Collection and Retention 294 13.2.2.9 Reporting Requirements and Information Sharing 295 13.2.2.10 Activity - Identify the Incident Response Plan Elements 296 13.2.2.11 Activity - Identify the Incident Handling Term 296 13.2.2.12 Activity - Identify the Incident Handling Step 296 13.2.2.13 Lab - Incident Handling 296 13.3 Summary 296 9781587134371 TOC 3/7/2018
£35.04
Nova Science Publishers Inc Data Management in the Semantic Web
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Nova Science Publishers Inc Electronic Health Records: Interoperability Plans
Book SynopsisElectronic health records (EHRs) play an important role in optimizing the health care provided to active duty servicemembers and veterans. When a servicemember leaves military service by way of discharge, separation, or retirement he or she may become eligible for VA benefits and services including VA health care. Transitioning their health care information from one large health care system (Department of Defense; DOD) to the other (Department of Veterans Affairs; VA) involves coordination of data and information between DOD and VA. Longstanding concern that this exchange be effective has been expressed in many quarters, including Congress. The purpose of this book is to provide a background on the long-standing efforts in sharing health information between DOD and VA. The book also describes changes to the integrated electronic health record system and evaluates the departments'' current plans; and determines whether the departments are effectively collaborating on management of the program.
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Nova Science Publishers Inc Air Transport Safety: An Introduction
Book SynopsisThis book is composed of thirteen chapters. Chapter 1 provides a short overview of the Air Transport System, from both the micro- and macro-level structure. It covers the descriptions and definitions of the main system elements: air carriers, airports and air navigation service providers, as well as personnel, equipment, procedures and the environment. Chapter 2 introduces the reader to the basic concepts in air transport risk and safety. This chapter covers the definitions of safety, hazards, risk, incidents and accidents. It further explains safety criteria, safety barriers, safety regulatory requirements, and finally it compares Safety I and Safety II concepts. Chapter 3 covers the field of Air Transport Safety Metrics and Records. Here safety metrics, accident statistics and safety records are explained and illustrated. Finally, a safety comparison of transport modes is made. Chapter 4 presents Sources of Accident/Incident Information. Explained here is how safety-related events (incidents and accidents) are investigated and what the phases of the investigation process are, as well as how safety information is collected. Chapter 5 describes the main safety issues in contemporary air transport. They are grouped into three sets: airport, air navigation service providers and air carriers'' safety issues.
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Technics Publications LLC Versión en español de la Guía DAMA de los
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Technics Publications LLC Data Resource Understanding: Utilizing the Data
Book SynopsisAre you struggling to understand the data you need to support your business activities? Are you frustrated over data that do not answer your questions or provide the wrong answers to your questions? Are you worried that your organisation is not adequately supporting its citizens or customers? Are you concerned over civil or criminal liability for the quality and use of your data? If the answer to any of these questions is Yes, they you need to read "Data Resource Understanding" to help you and everyone in your organisation thoroughly understand the data they need to support the business activities. Most public and private sector organisations have no formal method for thoroughly understanding the data needed to support their business activities. They seldom have a method that begins with the organisation''s perception of the business world and continues through a formal Data Resource Development Cycle to produce a high quality, thoroughly understood data resource that fully supports the organisation''s current and future business information demand. Data Resource Data provided the complete detailed data resource model for understanding and managing data as a critical resource of the organisation. Data Resource Understanding is the companion book to Data Resource Data. It provides a detailed explanation of how to thoroughly understand an organisation''s data resource and to document that understanding with Data Resource Data. Together they provide an organisation with the foundation for properly managing their data as a critical resource. Like in "Data Resource Simplexity", Michael Brackett draws on over half a century of data management experience, in a wide variety of different public and private sector organisations, to understand and document an organisation''s data resource. He leverages theories, concepts, principles, and techniques from many different and varied disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to the process of formally documenting an organisation''s data resource.
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Technics Publications LLC Data Modeling Made Simple with Embarcadero ER/Studio Data Architect: Adapting to Agile Data Modeling in a Big Data World
Book SynopsisBuild a working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio. This second edition includes numerous updates and new sections including an overview of ER/Studio''s support for agile development, as well as a description of some of ER/Studio''s newer features for NoSQL, such as MongoDB''s containment structure. You will build many ER/Studio data models along the way, applying best practices to master these ten objectives: 1. Know why a data model is needed and which ER/Studio models are the most appropriate for each situation. 2. Understand each component on the data model and how to represent and create them in ER/Studio. 3. Know how to leverage ER/Studio''s latest features including those assisting agile teams and forward and reverse engineering of NoSQL databases. 4. Know how to apply all the foundational features of ER/Studio. 5. Be able to build relational and dimensional conceptual, logical, and physical data models in ER/Studio. 6. Be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design. 7. Improve data model quality and impact analysis results by leveraging ER/Studio''s lineage functionality and compare/merge utility. 8. Be able to apply ER/Studio''s data dictionary features 9. Learn ways of sharing the data model through reporting and through exporting the model in a variety of formats. 10.Leverage ER/Studio''s naming functionality to improve naming consistency, including the new Automatic Naming Translation feature. This book contains four sections: Section I introduces data modeling and the ER/Studio landscape. Learn why data modeling is so critical to software development and even more importantly, why data modeling is so critical to understanding the business. You will learn about the newest features in ER/Studio (including features on big data and agile), and the ER/Studio environment. By the end of this section, you will have created and saved your first data model in ER/Studio and be ready to start modeling in Section II! Section II explains all of the symbols and text on a data model, including entities, attributes, relationships, domains, and keys. By the time you finish this section, you will be able to read'' a data model of any size or complexity, and create a complete data model in ER/Studio. Section III explores the three different levels of models: conceptual, logical, and physical. A conceptual data model (CDM) represents a business need within a defined scope. The logical data model (LDM) represents a detailed business solution, capturing the business requirements without complicating the model with implementation concerns such as software and hardware. The physical data model (PDM) represents a detailed technical solution. The PDM is the logical data model compromised often to improve performance or usability. The PDM makes up for deficiencies in our technology. By the end of this section you will be able to create conceptual, logical, and physical data models in ER/Studio. Section IV discusses additional features of ER/Studio. These features include data dictionary, data lineage, automating tasks, repository and portal, exporting and reporting, naming standards, and compare and merge functionality.
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Nova Science Publishers Inc E-Records Integrity Requirements
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Technics Publications LLC DAMA Dictionary of Data Management: 2nd Editio
Book SynopsisA glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining; Architecture; Artificial Intelligence; Business Analysis; DAMA & Professional Development; Databases & Database Design; Database Administration; Data Governance & Stewardship; Data Management; Data Modeling; Data Movement & Integration; Data Quality Management; Data Security Management; Data Warehousing & Business Intelligence; Document, Record & Content Management; Finance & Accounting; Geospatial Data; Knowledge Management; Marketing & Customer Relationship Management; Meta-Data Management; Multi-dimensional & OLAP; Normalization; Object-Orientation; Parallel Database Processing; Planning; Process Management; Project Management; Reference & Master Data Management; Semantic Modeling; Software Development; Standards Organizations; Structured Query Language (SQL); XML Development.
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Technics Publications LLC DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK): Portuguese Edition
Book SynopsisText in Portuguese. Written by over 120 data management practitioners, the ''DAMA Guide to the Data Management Body of Knowledge'' (DAMA-DMBOK) is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure.
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Technics Publications LLC Analytical Puzzle: Profitable Data Warehousing,
Book SynopsisDo you enjoy completing puzzles? Perhaps one of the most challenging (yet rewarding) puzzles is delivering a successful data warehouse suitable for data mining and analytics. The Analytical Puzzle describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices. This book describes an unbiased, practical, and comprehensive approach to building a data warehouse which will lead to an increased level of business intelligence within your organisation. New technologies continuously impact this approach and therefore this book explains how to leverage big data, cloud computing, data warehouse appliances, data mining, predictive analytics, data visualisation and mobile devices.
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PHI Learning Database Management Systems
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BPB Publications Linear Programming for Project Management
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BPB Publications IBM DB2 Administration Guide: Installation, Upgrade and Configuration of IBM DB2 on RHEL 8, Windows 10 and IBM Cloud
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BPB Publications DBA Essentials for 21c: Installing Oracle
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Universities Press Data Science and Analytics with Python
Book SynopsisSalient features: Ideal for undergraduate courses on Data Science and Analytics; Provides step-by-step instructions for setting up the Python environment and executing various libraries and packages;
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Universities Press Big Data Analytics
Book SynopsisBig Data Analytics is intended for use as a textbook for third- and fourth-year students of B.E., B.Tech., B.Sc., BCA, MCA, and M.Tech. courses in IT, Software, and Computer Science Engineering. The book has been written to help students who enter the software industry to gain a broad understanding of Big Data and the nuances of handling it to extract useful information. Spread across 21 chapters, it elucidates the concept of Big Data and walks the reader through popular frameworks such as Hadoop, MongoDB, Pig and Hive that are used for processing Big Data.
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McGraw-Hill Education - Europe Carl Youngs Adobe Acrobat 6.0
Book SynopsisWritten for those with Acrobat experience, and seeking to take advantage of the feature enhancements of either the Standard or Professional version of Acrobat 6.0. This work teaches the techniques for creating professional PDFs for print, the web, or CD. The author produces the Adobe-supported PDF Conference.Table of ContentsPart I: PDF Standards for Everyone 1 What's New in the Acrobat and PDF Universe 2 Why Quality Matters 3 Creating the Best PDF for the Job 4 Making Onscreen PDFs 5 Making a PDF Everyone Can Read Part II: In Business with Acrobat 6 Putting Acrobat to Work 7 Working Together: Acrobat Collaboration 8 PDF from Corel WordPerfect 9 Working with Acrobat Security and Digital Signatures 10 The Wide World of Acrobat 11 Moving Beyond One-Document-at-a-Time Creation 12 PDF Forms 13 Introduction to Adobe Acrobat JavaScript 14 A Short Course for System Administrators Part III: Acrobat for Creative Professionals 15 Adobe Products 16 Corel Applications 17 PDF from QuarkXPress 18 Preflighting and Color Printing
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