Databases / Data management Books

Book SynopsisThis book constitutes the thoroughly refereed post-conference proceedings of the Satellite Events of the 14th European Conference on the Semantic Web, ESWC 2017, held in Portoroz, Slovenia, in May/June2017.The volume contains 8 poster and 24 demonstration papers, selected from 105 submissions. Additionally, this book includes a selection of 13 best workshop papers. The papers cover various aspects of the semantic web.The chapter 'Scholia, Scientometrics and Wikidata' is available open access under a CC BY 4.0 license via link.springer.com.Table of ContentsQuerying the Web of Data.- Semantic Web Solutions for Large-Scale Biomedical Data Analytics.- Scientometrics.- RDF Stream Processing.- Emotions, Modality, Sentiment Analysis and the Semantic Web.- Applications of Semantic Web Technologies in Robotics.- Linked Data and Distributed Ledgers.- Linked Data Quality.- Semantic Deep Learning.- Humanities in the Semantic Web.

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Book SynopsisThe target of SQL tuning is the improvement of the existing execution plan. The authors discuss the removal of brakes in the execution plan. Such "brakes" or bottlenecks can be recognized by a formal analysis of the execution plan. For this purpose no data model knowledge is needed. This is especially beneficial for database administrators because they are usually not or insufficiently familiar with the data model. The book presents numerous practical examples with this method.Table of ContentsPrefaces.- Introduction.- Some thoughts concerning the term "SQL Tuning".- Minimum minimorum to the subject "Execution Plan".- Approaches to formal SQL Tuning.- Bottlenecks in Execution Plans.- Procedure for formal SQL Tuning.- Practical experiences with formal SQL Tuning.- Appendix.- Application of the formal method for analysis of performance issues following Oracle migration.

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Book SynopsisIn the era of propelling traditional energy systems to evolve towards smart energy systems, including power generation, energy storage systems, and electricity consumption have become more dynamic. The quality and reliability of power supply are impacted by the sporadic and rising use of electric vehicles, domestic loads, and industrial loads. Similarly, with the integration of solid state devices, renewable sources, and distributed generation, power generation processes are evolving in a variety of ways. Several cutting-edge technologies are necessary for the safe and secure operation of power systems in such a dynamic setting, including load distribution, automation, energy regulation & control, and energy trading. This book covers the applications of various big data analytics,artificial intelligence, and machine learning technologies in smart grids for demand prediction, decision-making processes, policy, and energy management. The book delves into the new technologies for modern power systems such as the Internet of Things, Blockchain for smart home and smart city solutions in depth.Technical topics discussed in the book include:• Hybrid smart energy system technologies• Smart meters• Energy demand forecasting• Use of different protocols and communication in smart energysystems• Power quality and allied issues and mitigation using AI• Intelligent transportation• Virtual power plants• AI based smart energy business models• Smart home solutions• Blockchain solutions for smart grids.Table of Contents1. Introduction to Smart Energy Systems and Recent Trends 2. An Overview of Artificial Intelligence, Big Data, and Internet of Things for Future Energy Systems 3. LoRa: A New Technology for Smart Grid Applications 4. Clustering Hybrid Application for Load Forecasting in Smart Grids 5. Big Data Analytical Techniques for Electrical Energy Forecasting in the Smart Grid Paradigm 6. A Review of Smart Grid Planning Approaches and a New Proposal for operational Planning of a Smart Grid with Smart Homes 7. Smart Meter Data Analytics Case Study: Identification of LV Distribution Network Topology to Design Optimal Planning Solutions 8. Build Smart Grids on Artificial Intelligence: A Real-world Example 9. Artificial Intelligence Enabled Energy-efficient Technologies for Secured Smart Homes 10. A Review of Technologies in Net Zero Energy Building for Islanded Operation

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Book SynopsisExplore the engineering mechanism of blockchain, cryptocurrency, and Ethereum. Know-how of peer-to-peer networks, IPFS, and decentralized databases. Explore the workings of DApps and build your own blockchain app.

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Book SynopsisThis book provides an overview of crowdsourced data management. Covering all aspects including the workflow, algorithms and research potential, it particularly focuses on the latest techniques and recent advances. The authors identify three key aspects in determining the performance of crowdsourced data management: quality control, cost control and latency control. By surveying and synthesizing a wide spectrum of studies on crowdsourced data management, the book outlines important factors that need to be considered to improve crowdsourced data management. It also introduces a practical crowdsourced-database-system design and presents a number of crowdsourced operators. Self-contained and covering theory, algorithms, techniques and applications, it is a valuable reference resource for researchers and students new to crowdsourced data management with a basic knowledge of data structures and databases.Table of Contents1. Introduction.- 2. Crowdsourcing Background. 3. Quality Control.- 4. Cost Control.- 5. Latency Control.- 6. Crowdsourcing Database Systems and Optimization.- 7. Crowdsourced Operators.- Conclusion.

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Book SynopsisPreface.- Acknowledgements.- Part 1. Communication over the Gaussian channel.- Chapter 1.Overview of the book.- Chapter 2. A statistical model for additive noise channels.- Chapter 3. Additive Gaussian noise model.- Problem Set 1.- Chapter 4. Optimal receiver: maximum A Posteriori (MAP) principle.- Chapter 5. Analysis of error probability.- Chapter 6. Multiple bits transmission via pulse amplitude modulation.- Problem Set 2.- Chapter 7. Multi-shot communication.- Chapter 8. Repetition coding.- Chapter 9: Capacity of the additive white Gaussian noise channel.- Problem Set 3.- Part 2. Communication over inter-symbol interference (ISI) channels.- Chapter 10. Signal conversion from discrete to continuous time (1/2).- Chapter 11. Signal conversion from discrete to continuous time (2/2).- Chapter 12. Optimal receiver architecture.- Problem Set 4.- Chapter 13. Optimal receiver in ISI channels: maximum likelihood (ML) sequence detection.- Chapter&nb

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Book SynopsisChapter 1 Introduction.- Chapter 2 Sets and Functions.- Chapter 3 Liner Algebra.- Chapter 4 Matrix Decomposition.- Chapter 5 Calculus.- Chapter 6 Advanced Calculus.- Chapter 7 Algorithms 1 – Principal Component Analysis.- Chapter 8 Algorithms 2 – Liner Regression.- Chapter 9 Algorithms 3 – Neural Networks.- Chapter 10 Probability.- Chapter 11 Further Probability.- Chapter 12 Elements of Statistics.- Chapter 13 Algorithms 4 – Maximum Likelihood Estimation and its Application to Regression.- Chapter 14 Data Modelling in Practice.

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Book SynopsisAt its core, machine learning is about efficiently identifying patterns and relationships in data. Many tasks, such as finding associations among terms so you can make accurate search recommendations or locating individuals within a social network who have similar interests, are naturally expressed as graphs. Graph-Powered Machine Learning introduces you to graph technology concepts, highlighting the role of graphs in machine learning and big data platforms. You’ll get an in-depth look at techniques including data source modeling, algorithm design, link analysis, classification, and clustering. As you master the core concepts, you’ll explore three end-to-end projects that illustrate architectures, best design practices, optimization approaches, and common pitfalls. Key Features · The lifecycle of a machine learning project · Three end-to-end applications · Graphs in big data platforms · Data source modeling · Natural language processing, recommendations, and relevant search · Optimization methods Readers comfortable with machine learning basics. About the technology By organizing and analyzing your data as graphs, your applications work more fluidly with graph-centric algorithms like nearest neighbor or page rank where it’s important to quickly identify and exploit relevant relationships. Modern graph data stores, like Neo4j or Amazon Neptune, are readily available tools that support graph-powered machine learning. Alessandro Negro is a Chief Scientist at GraphAware. With extensive experience in software development, software architecture, and data management, he has been a speaker at many conferences, such as Java One, Oracle Open World, and Graph Connect. He holds a Ph.D. in Computer Science and has authored several publications on graph-based machine learning.

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Book SynopsisDiscover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R.Beginning Data Science in R 4, Second Editiondetails how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this.Modern data analysis requires computational skills and usually a minimum of programming. After reading and using this book, you'll have what you need to get started with R programming with data science applications. Source code will be available to support your next projects as well. Source code is available at github.cTable of Contents1. Introduction to R programming. 2. Reproducible analysis. 3. Data manipulation. 4. Visualizing and exploring data. 5. Working with large data sets.6. Supervised learning. 7. Unsupervised learning. 8. More R programming.9. Advanced R programming.10. Object oriented programming.11. Building an R package.12. Testing and checking. 13. Version control. 14. Profiling and optimizing.

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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

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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

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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.

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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.

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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.

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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

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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

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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

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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.

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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

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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

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Book SynopsisBeginning-Intermediate user levelTable of Contents​Chapter 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

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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

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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

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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

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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

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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

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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 .....................................................................................................

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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

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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.

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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

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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

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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

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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

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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.

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