Scalable Data Analytics with Azure Data Explorer
Write efficient and powerful KQL queries to query and visualize your data and implement best practices to improve KQL execution performanceKey Features: Apply Azure Data Explorer best practices to manage your data at scale and reduce KQL execution timeDiscover how to query and visualize your data using the powerful KQLManage cluster performance and monthly costs by understanding how to size your ADX cluster correctlyBook Description: Azure Data Explorer (ADX) enables developers and data scientists to make data-driven business decisions. This book will help you rapidly explore and query your data at scale and secure your ADX clusters.The book begins by introducing you to ADX, its architecture, core features, and benefits. You'll learn how to securely deploy ADX instances and navigate through the ADX Web UI, cover data ingestion, and discover how to query and visualize your data using the powerful Kusto Query Language (KQL). Next, you'll get to grips with KQL operators and functions to efficiently query and explore your data, as well as perform time series analysis and search for anomalies and trends in your data. As you progress through the chapters, you'll explore advanced ADX topics, including deploying your ADX instances using Infrastructure as Code (IaC). The book also shows you how to manage your cluster performance and monthly ADX costs by handling cluster scaling and data retention periods. Finally, you'll understand how to secure your ADX environment by restricting access with best practices for improving your KQL query performance.By the end of this Azure book, you'll be able to securely deploy your own ADX instance, ingest data from multiple sources, rapidly query your data, and produce reports with KQL and Power BI.What You Will Learn: Become well-versed with the core features of the Azure Data Explorer architectureDiscover how ADX can help manage your data at scale on AzureGet to grips with deploying your ADX environment and ingesting and analyzing your dataExplore KQL and learn how to query your dataQuery and visualize your data using the ADX UI and Power BIIngest structured and unstructured data types from an array of sourcesUnderstand how to deploy, scale, secure, and manage ADXWho this book is for: This book is for data analysts, data engineers, and data scientists who are responsible for analyzing and querying their team's large volumes of data on Azure. SRE and DevOps engineers who deploy, maintain, and secure infrastructure will also find this book useful. Prior knowledge of Azure and basic data querying will help you to get the most out of this book.
Computer Models of Process Dynamics
COMPUTER MODELS OF PROCESS DYNAMICS Comprehensive overview of techniques for describing physical phenomena by means of computer models that are determined by mathematical analysis Computer Models of Process Dynamics covers everything required to do computer based mathematical modeling of dynamic systems, including an introduction to a scientific language, its use to program essential operations, and methods to approximate the integration of continuous signals. From a practical standpoint, readers will learn how to build computer models that simulate differential equations. They are also shown how to model physical objects of increasing complexity, where the most complex objects are simulated by finite element models, and how to follow a formal procedure in order to build a valid computer model. To aid in reader comprehension, a series of case studies is presented that covers myriad different topics to provide a view of the challenges that fall within this discipline. The book concludes with a discussion of how computer models are used in an engineering project where the readers would operate in a team environment. Other topics covered in Computer Models of Process Dynamics include: Computer hardware and software, covering algebraic expressions, math functions, computation loops, decision-making, graphics, and user-defined functions Creative thinking and scientific theories, covering the Ancients, the Renaissance, Galileo, Newton, electricity and magnetism, and newer sciences Uncertainty and softer science, covering random number generators, statistical analysis of data, the method of least squares, and state/velocity estimators Flight simulators, covering the motion of an aircraft, the equations of motion, short period pitching motion, and phugoid motion Established engineers and programmers, along with students and academics in related programs of study, can harness the comprehensive information in Computer Models of Process Dynamics to gain mastery over the subject and be ready to use their knowledge in many practical applications in the field.
Data Engineering with Google Cloud Platform
Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineerKey Features: Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solutionLearn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelinesDiscover tips to prepare for and pass the Professional Data Engineer examBook Description: With this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards.Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP.By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What You Will Learn: Load data into BigQuery and materialize its output for downstream consumptionBuild data pipeline orchestration using Cloud ComposerDevelop Airflow jobs to orchestrate and automate a data warehouseBuild a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc clusterLeverage Pub/Sub for messaging and ingestion for event-driven systemsUse Dataflow to perform ETL on streaming dataUnlock the power of your data with Data StudioCalculate the GCP cost estimation for your end-to-end data solutionsWho this book is for: This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.
Exploring Big Historical Data: The Historian’s Macroscope (Second Edition)
Every day, more and more kinds of historical data become available, opening exciting new avenues of inquiry but also new challenges. This updated and expanded book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Originally authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.
Exploring Big Historical Data: The Historian’s Macroscope (Second Edition)
Every day, more and more kinds of historical data become available, opening exciting new avenues of inquiry but also new challenges. This updated and expanded book describes and demonstrates the ways these data can be explored to construct cultural heritage knowledge, for research and in teaching and learning. It helps humanities scholars to grasp Big Data in order to do their work, whether that means understanding the underlying algorithms at work in search engines or designing and using their own tools to process large amounts of information.Demonstrating what digital tools have to offer and also what 'digital' does to how we understand the past, the authors introduce the many different tools and developing approaches in Big Data for historical and humanistic scholarship, show how to use them, what to be wary of, and discuss the kinds of questions and new perspectives this new macroscopic perspective opens up. Originally authored 'live' online with ongoing feedback from the wider digital history community, Exploring Big Historical Data breaks new ground and sets the direction for the conversation into the future.Exploring Big Historical Data should be the go-to resource for undergraduate and graduate students confronted by a vast corpus of data, and researchers encountering these methods for the first time. It will also offer a helping hand to the interested individual seeking to make sense of genealogical data or digitized newspapers, and even the local historical society who are trying to see the value in digitizing their holdings.
Frontiers of Algorithmics
This book constitutes the proceedings of the 15th International Workshop on Frontiers in Algorithmics, FAW 2021, held in conjunction with second International Joint Conference on Theoretical Computer Science (IJTCS 2021), as IJTCS-FAW 2021, in Beijing, China, in August 2021. The conference IJTCS-FAW 2021 was held in hybrid mode due to the COVID-19 pandemic. The 5 full papers presented in this volume were carefully reviewed and selected from 9 submissions. The joint conference provides a focused forum on Algorithmic Game Theory, Blockchain, Multi-agent Reinforcement Learning, Quantum Computation, Theory of Machine Learning, Machine Learning, Formal Method, Algorithm and Complexity, and EconCS.
Smart Cities
This book constitutes the thoroughly refereed proceedings of the 4th Ibero-American Congress, ICSC-CITIES 2021, held in Canc繳n, Mexico, in November - December 2021. Due to the COVID-19 pandemic the conference was partially held online. The 21 full papers and one short paper presented were carefully reviewed and selected from 112 submissions. The papers are organized in topical sections on ​computational intelligence for smart cities; urban informatics; internet of things, smart energy and smart grid.
Internet of Things. Technology and Applications
This book constitutes the refereed post-conference proceedings of the Fourth IFIP International Cross-Domain Conference on Internet of Things, IFIPIoT 2021, held virtually in November 2021. The 15 full papers presented were carefully reviewed and selected from 33 submissions. Also included is a summary of two panel sessions held at the conference. The papers are organized in the following topical sections: challenges in IoT Applications and Research, Modernizing Agricultural Practice Using IoT, Cyber-physical IoT systems in Wildfire Context, IoT for Smart Health, Security, Methods.
Database Dreaming Volume I
Along with its companion volume (Database Dreaming Volume II), this book offers a collection of essays on the general topic of relational databases and relational database technology. Most of those essays, though not all, have been published before, but only in journals and magazines that are now hard to find or in books that are now out of print. Here's a lightly edited excerpt from the preface (so this is the author speaking): I went back and reviewed all of those early essays, looking for ones that seemed worth reviving (or, rather, revising and reviving) at this time. Of course, some of them definitely weren't! However, out of a total of around 130 original papers, I did find some 20 or so that seemed to me worth preserving and hadn't already been incorporated in, or superseded by, more recent books of mine. So I tracked down the original versions of those 20 or so papers and set to work. When I was done, though, I found I had somewhere in excess of 600 pages on my hands-too much, in my view, for just one book, and so I split them across two separate volumes. Highlights of the present volume include a discussion of the difficulties involved in providing a relational interface to a nonrelational system; a tutorial on the quantifiers and what happens to them under three-valued logic; an examination of the effect of user defined types on optimization; some thoughts on normalization and database design tools; and caveats regarding certain important database operators, especially outer join and negation.
Database Dreaming Volume II
Along with its companion volume (Database Dreaming Volume I), this book offers a collection of essays on the general topic of relational databases and relational database technology. Most of those essays, though not all, have been published before, but only in journals and magazines that are now hard to find or in books that are now out of print. Here's a lightly edited excerpt from the preface (so this is the author speaking): I went back and reviewed all of those early essays, looking for ones that seemed worth reviving (or, rather, revising and reviving) at this time. Of course, some of them definitely weren't! However, out of a total of around 130 original papers, I did find some 20 or so that seemed to me worth preserving and hadn't already been incorporated in, or superseded by, more recent books of mine. So I tracked down the original versions of those 20 or so papers and set to work. When I was done, though, I found I had somewhere in excess of 600 pages on my hands-too much, in my view, for just one book, and so I split them across two separate volumes. Highlights of the present volume include a detailed explanation of the multiple assignment operator and why it's so essential; an investigation into why object and database technologies are so much more different than they're often made out to be; a critical examination of SQL's support for pointers ("references"); a tutorial on the counterintuitive (but crucial) concept of tables with no columns; and an annotated and extended debate between the author and E. F. Codd, inventor of the relational model, on the subject of nulls and three-valued logic.
Getting Started with Elastic Stack 8.0
Use the Elastic Stack for search, security, and observability-related use cases while working with large amounts of data on-premise and on the cloudKey Features: Learn the core components of the Elastic Stack and how they work togetherBuild search experiences, monitor and observe your environments, and defend your organization from cyber attacksGet to grips with common architecture patterns and best practices for successfully deploying the Elastic StackBook Description: The Elastic Stack helps you work with massive volumes of data to power use cases in the search, observability, and security solution areas.This three-part book starts with an introduction to the Elastic Stack with high-level commentary on the solutions the stack can be leveraged for. The second section focuses on each core component, giving you a detailed understanding of the component and the role it plays. You'll start by working with Elasticsearch to ingest, search, analyze, and store data for your use cases. Next, you'll look at Logstash, Beats, and Elastic Agent as components that can collect, transform, and load data. Later chapters help you use Kibana as an interface to consume Elastic solutions and interact with data on Elasticsearch. The last section explores the three main use cases offered on top of the Elastic Stack. You'll start with a full-text search and look at real-world outcomes powered by search capabilities. Furthermore, you'll learn how the stack can be used to monitor and observe large and complex IT environments. Finally, you'll understand how to detect, prevent, and respond to security threats across your environment. The book ends by highlighting architecture best practices for successful Elastic Stack deployments.By the end of this book, you'll be able to implement the Elastic Stack and derive value from it.What You Will Learn: Configure Elasticsearch clusters with different node types for various architecture patternsIngest different data sources into Elasticsearch using Logstash, Beats, and Elastic AgentBuild use cases on Kibana including data visualizations, dashboards, machine learning jobs, and alertsDesign powerful search experiences on top of your data using the Elastic StackSecure your organization and learn how the Elastic SIEM and Endpoint Security capabilities can helpExplore common architectural considerations for accommodating more complex requirementsWho this book is for: Developers and solutions architects looking to get hands-on experience with search, security, and observability-related use cases on the Elastic Stack will find this book useful. This book will also help tech leads and product owners looking to understand the value and outcomes they can derive for their organizations using Elastic technology. No prior knowledge of the Elastic Stack is required.
Data Lakehouse in Action
Propose a new scalable data architecture paradigm, Data Lakehouse, that addresses the limitations of current data architecture patternsKey Features: Understand how data is ingested, stored, served, governed, and secured for enabling data analyticsExplore a practical way to implement Data Lakehouse using cloud computing platforms like AzureCombine multiple architectural patterns based on an organization's needs and maturity levelBook Description: The Data Lakehouse architecture is a new paradigm that enables large-scale analytics. This book will guide you in developing data architecture in the right way to ensure your organization's success.The first part of the book discusses the different data architectural patterns used in the past and the need for a new architectural paradigm, as well as the drivers that have caused this change. It covers the principles that govern the target architecture, the components that form the Data Lakehouse architecture, and the rationale and need for those components. The second part deep dives into the different layers of Data Lakehouse. It covers various scenarios and components for data ingestion, storage, data processing, data serving, analytics, governance, and data security. The book's third part focuses on the practical implementation of the Data Lakehouse architecture in a cloud computing platform. It focuses on various ways to combine the Data Lakehouse pattern to realize macro-patterns, such as Data Mesh and Data Hub-Spoke, based on the organization's needs and maturity level. The frameworks introduced will be practical and organizations can readily benefit from their application.By the end of this book, you'll clearly understand how to implement the Data Lakehouse architecture pattern in a scalable, agile, and cost-effective manner.What You Will Learn: Understand the evolution of the Data Architecture patterns for analyticsBecome well versed in the Data Lakehouse pattern and how it enables data analyticsFocus on methods to ingest, process, store, and govern data in a Data Lakehouse architectureLearn techniques to serve data and perform analytics in a Data Lakehouse architectureCover methods to secure the data in a Data Lakehouse architectureImplement Data Lakehouse in a cloud computing platform such as AzureCombine Data Lakehouse in a macro-architecture pattern such as Data MeshWho this book is for: This book is for data architects, big data engineers, data strategists and practitioners, data stewards, and cloud computing practitioners looking to become well-versed with modern data architecture patterns to enable large-scale analytics. Basic knowledge of data architecture and familiarity with data warehousing concepts are required.
Azure Data Engineer Associate Certification Guide
Become well-versed with data engineering concepts and exam objectives to achieve Azure Data Engineer Associate certificationKey Features: Understand and apply data engineering concepts to real-world problems and prepare for the DP-203 certification examExplore the various Azure services for building end-to-end data solutionsGain a solid understanding of building secure and sustainable data solutions using Azure servicesBook Description: Azure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other.Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you'll work on sample questions and answers to familiarize yourself with the pattern of the exam.By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.What You Will Learn: Gain intermediate-level knowledge of Azure the data infrastructureDesign and implement data lake solutions with batch and stream pipelinesIdentify the partition strategies available in Azure storage technologiesImplement different table geometries in Azure Synapse AnalyticsUse the transformations available in T-SQL, Spark, and Azure Data FactoryUse Azure Databricks or Synapse Spark to process data using NotebooksDesign security using RBAC, ACL, encryption, data masking, and moreMonitor and optimize data pipelines with debugging tipsWho this book is for: This book is for data engineers who want to take the DP-203: Azure Data Engineer Associate exam and are looking to gain in-depth knowledge of the Azure cloud stack.The book will also help engineers and product managers who are new to Azure or interviewing with companies working on Azure technologies, to get hands-on experience of Azure data technologies. A basic understanding of cloud technologies, extract, transform, and load (ETL), and databases will help you get the most out of this book.
Beginning Relational Data Modeling
Data storage design, and awareness of how data needs to be utilized within an organization, is of prime importance in ensuring that company data systems work efficiently. Beginning Relational Data Modeling will lead you step by step through the process of developing an effective logical data model for your relational database model.
Exploring Graphs with Elixir
Data is everywhere - it's just not very well connected, which makes it super hard to relate dataset to dataset. Using graphs as the underlying glue, you can readily join data together and create navigation paths across diverse sets of data. Add Elixir, with its awesome power of concurrency, and you'll soon be mastering data networks. Learn how different graph models can be accessed and used from within Elixir and how you can build a robust semantics overlay on top of graph data structures. We'll start from the basics and examine the main graph paradigms. Get ready to embrace the world of connected data! Graphs provide an intuitive and highly flexible means for organizing and querying huge amounts of loosely coupled data items. These data networks, or graphs in math speak, are typically stored and queried using graph databases. Elixir, with its noted support for fault tolerance and concurrency, stands out as a language eminently suited to processing sparsely connected and distributed datasets. Using Elixir and graph-aware packages in the Elixir ecosystem, you'll easily be able to fit your data to graphs and networks, and gain new information insights. Build a testbed app for comparing native graph data with external graph databases. Develop a set of applications under a single umbrella app to drill down into graph structures. Build graph models in Elixir, and query graph databases of various stripes - using Cypher and Gremlin with property graphs and SPARQL with RDF graphs. Transform data from one graph modeling regime to another. Understand why property graphs are especially good at graph traversal problems, while RDF graphs shine at integrating different semantic models and can scale up to web proportions. Harness the outstanding power of concurrent processing in Elixir to work with distributed graph datasets and manage data at scale. What You Need: To follow along with the book, you should have Elixir 1.10+ installed. The book will guide you through setting up an umbrella application for a graph testbed using a variety of graph databases for which Java SDK 8+ is generally required. Instructions for installing the graph databases are given in an appendix.
Mobile Networks and Management
This book constitutes the refereed post-conference proceedings of the 11th International Conference on Mobile Networks and Management, MONAMI 2021, held in October 2021. The conference was held virtually due to the COVID-19 pandemic. The 26 full papers were carefully reviewed and selected from 53 submissions. The papers are divided into groups of content as follows: The application of artificial intelligence for smart city; Advanced technology in edge and fog computing; Emerging technologies and applications in mobile networks and management; and Recent advances in communications and computing.
Fundamentals of Logic and Computation
This textbook aims to help the reader develop an in-depth understanding of logical reasoning and gain knowledge of the theory of computation. The book combines theoretical teaching and practical exercises; the latter is realised in Isabelle/HOL, a modern theorem prover, and PAT, an industry-scale model checker. I also give entry-level tutorials on the two software to help the reader get started. By the end of the book, the reader should be proficient in both software. Content-wise, this book focuses on the syntax, semantics and proof theory of various logics; automata theory, formal languages, computability and complexity. The final chapter closes the gap with a discussion on the insight that links logic with computation. This book is written for a high-level undergraduate course or a Master's course. The hybrid skill set of practical theorem proving and model checking should be helpful for the future of readers should they pursue a research career or engineering informal methods.
Knowledge-Based Explorable Extended Reality Environments
This book presents explorable XR environments--their rationale, concept, architectures as well as methods and tools for spatial-temporal composition based on domain knowledge, including geometrical, presentational, structural and behavioral elements. Explorable XR environments enable monitoring, analyzing, comprehending, examining and controlling users' and objects' behavior and features as well as users' skills, experience, interests and preferences.The E-XR approach proposed in this book relies on two main pillars. The first is knowledge representation technologies, such as logic programming, description logics and the semantic web, which permit automated reasoning and queries. The second is imperative programming languages, which are a prevalent solution for building XR environments. Potential applications of E-XR are in a variety of domains, e.g., education, training, medicine, design, tourism, marketing, merchandising, engineering and entertainment.The book's readers will understand the emerging domain of explorable XR environments with their possible applications. Special attention is given to an in-depth discussion of the field with taxonomy and classification of the available related solutions. Examples and design patterns of knowledge-based composition and exploration of XR behavior are provided, and an extensive evaluation and analysis of the proposed approach is included. This book helps researchers in XR systems, 3D modeling tools and game engines as well as lecturers and students who search for clearly presented information supported by use cases. For XR and game programmers as well as graphic designers, the book is a valuable source of information and examples in XR development. Professional software and web developers may find the book interesting as the proposed ideas are illustrated by rich examples demonstrating design patterns and guidelines in object-oriented, procedural and declarative programming.
The Trouble with Big Data
Choice Outstanding Academic Title 2023 This open access book explores the challenges society faces with big data, through the lens of culture rather than social, political or economic trends, as demonstrated in the words we use, the values that underpin our interactions, and the biases and assumptions that drive us. Focusing on areas such as data and language, data and sensemaking, data and power, data and invisibility, and big data aggregation, it demonstrates that humanities research, focusing on cultural rather than social, political or economic frames of reference for viewing technology, resists mass datafication for a reason, and that those very reasons can be instructive for the critical observation of big data research and innovation. The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by Trinity College Dublin, DARIAH-EU and the European Commission.
Extreme DAX
Discover the true power of DAX and build advanced DAX solutions for practical business scenariosKey Features: Solve complex business problems within Microsoft BI tools including Power BI, SQL Server, and ExcelDevelop a conceptual understanding of critical business data modeling principlesLearn the subtleties of Power BI data visualizations, evaluation context, context transition, and filteringBook Description: This book helps business analysts generate powerful and sophisticated analyses from their data using DAX and get the most out of Microsoft Business Intelligence tools.Extreme DAX will first teach you the principles of business intelligence, good model design, and how DAX fits into it all. Then, you'll launch into detailed examples of DAX in real-world business scenarios such as inventory calculations, forecasting, intercompany business, and data security. At each step, senior DAX experts will walk you through the subtleties involved in working with Power BI models and common mistakes to look out for as you build advanced data aggregations.You'll deepen your understanding of DAX functions, filters, and measures, and how and when they can be used to derive effective insights. You'll also be provided with PBIX files for each chapter, so that you can follow along and explore in your own time.What You Will Learn: Understand data modeling concepts and structures before you start working with DAXGrasp how relationships in Power BI models are different from those in RDBMSesSecure aggregation levels, attributes, and hierarchies using PATH functions and row-level securityGet to grips with the crucial concept of contextApply advanced context and filtering functions including TREATAS, GENERATE, and SUMMARIZEExplore dynamically changing visualizations with helper tables and dynamic labels and axesWork with week-based calendars and understand standard time-intelligenceEvaluate investments intelligently with the XNPV and XIRR financial DAX functionsWho this book is for: Extreme DAX is written for analysts with a working knowledge of DAX in Power BI or other Microsoft analytics tools. It will help you upgrade your knowledge and work with analytical models more effectively, so you'll need practical experience with DAX before you can get started.
The Hundred-Page Machine Learning Book
Peter Norvig, Research Director at Google, co-author of AIMA, the most popular AI textbook in the world: "Burkov has undertaken a very useful but impossibly hard task in reducing all of machine learning to 100 pages. He succeeds well in choosing the topics - both theory and practice - that will be useful to practitioners, and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field."Aur矇lien G矇ron, Senior AI Engineer, author of the bestseller Hands-On Machine Learning with Scikit-Learn and TensorFlow: "The breadth of topics the book covers is amazing for just 100 pages (plus few bonus pages!). Burkov doesn't hesitate to go into the math equations: that's one thing that short books usually drop. I really liked how the author explains the core concepts in just a few words. The book can be very useful for newcomers in the field, as well as for old-timers who can gain from such a broad view of the field."Karolis Urbonas, Head of Data Science at Amazon: "A great introduction to machine learning from a world-class practitioner."Chao Han, VP, Head of R&D at Lucidworks: "I wish such a book existed when I was a statistics graduate student trying to learn about machine learning."Sujeet Varakhedi, Head of Engineering at eBay: "Andriy's book does a fantastic job of cutting the noise and hitting the tracks and full speed from the first page.''Deepak Agarwal, VP of Artificial Intelligence at LinkedIn: "A wonderful book for engineers who want to incorporate ML in their day-to-day work without necessarily spending an enormous amount of time.''Gareth James, Professor of Data Sciences and Operations, co-author of the bestseller An Introduction to Statistical Learning, with Applications in R: "I would highly recommend "The Hundred-Page Machine Learning Book" for both the beginner looking to learn more about machine learning and the experienced practitioner seeking to extend their knowledge base."
Supercomputing
This book constitutes the refereed post-conference proceedings of the 7th Russian Supercomputing Days, RuSCDays 2021, held in Moscow, Russia, in September 2021.The 37 revised full papers and 3 short papers presented were carefully reviewed and selected from 99 submissions. The papers are organized in the following topical sections: supercomputer simulation; HPC, BigData, AI: architectures, technologies, tools; and distributed and cloud computing.
Logic Programming Languages
This collection of current research on logic programming languages presents results from a three-year, ESPRIT-funded effort to explore the integration of the foundational issues of functional, logic, and object-oriented programming. It offers valuable insights into the fast-developing extensions of logic programming with functions, constraints, concurrency, and objects. Chapters are grouped according to the unifying themes of functional programming, constraint, logic programming, and object-oriented programming.
Communication Technologies for Vehicles
This book constitutes the refereed proceedings of the 16th International Workshop on Communication Technologies for Vehicles, Nets4Cars/Nets4Trains/Nets4Aircraft 2021, held in Madrid, Spain, in November 2021. The 6 full and 2 short papers were carefully reviewed and selected from numerous submissions. The selected papers present original research results in areas related to the physical layer, communication protocols and standards, mobility and traffic models, experimental and field operational testing, and performance analysis
Algorithmic Aspects in Information and Management
This book constitutes the proceedings of the 15th International Conference on Algorithmic Aspects in Information and Management, AAIM 2021, which was held online during December 20-22, 2021. The conference was originally planned to take place in Dallas, Texas, USA, but changed to a virtual event due to the COVID-19 pandemic. The 38 regular papers included in this book were carefully reviewed and selected from 62 submissions. They were organized in the following topical sections: approximation algorithms; scheduling; nonlinear combinatorial optimization; network problems; blockchain, logic, complexity and reliability; and miscellaneous.
Building Big Data Pipelines with Apache Beam
Implement, run, operate, and test data processing pipelines using Apache BeamKey Features: Understand how to improve usability and productivity when implementing Beam pipelinesLearn how to use stateful processing to implement complex use cases using Apache BeamImplement, test, and run Apache Beam pipelines with the help of expert tips and techniquesBook Description: Apache Beam is an open source unified programming model for implementing and executing data processing pipelines, including Extract, Transform, and Load (ETL), batch, and stream processing.This book will help you to confidently build data processing pipelines with Apache Beam. You'll start with an overview of Apache Beam and understand how to use it to implement basic pipelines. You'll also learn how to test and run the pipelines efficiently. As you progress, you'll explore how to structure your code for reusability and also use various Domain Specific Languages (DSLs). Later chapters will show you how to use schemas and query your data using (streaming) SQL. Finally, you'll understand advanced Apache Beam concepts, such as implementing your own I/O connectors.By the end of this book, you'll have gained a deep understanding of the Apache Beam model and be able to apply it to solve problems.What You Will Learn: Understand the core concepts and architecture of Apache BeamImplement stateless and stateful data processing pipelinesUse state and timers for processing real-time event processingStructure your code for reusabilityUse streaming SQL to process real-time data for increasing productivity and data accessibilityRun a pipeline using a portable runner and implement data processing using the Apache Beam Python SDKImplement Apache Beam I/O connectors using the Splittable DoFn APIWho this book is for: This book is for data engineers, data scientists, and data analysts who want to learn how Apache Beam works. Intermediate-level knowledge of the Java programming language is assumed.
Power Pivot and Power Bi
Microsoft PowerPivot is a free add-on to Excel from Microsoft that allows users to produce new kinds of reports and analyses that were simply impossible before, and this book is the first to tackle DAX formulas, the core capability of PowerPivot, from the perspective of the Excel audience. Written by the world's foremost PowerPivot blogger and practitioner, the book's concepts and approach are introduced in a step-by-step manner tailored to the learning style of Excel users everywhere. The techniques presented allow users to produce, in hours or even minutes, results that formerly would have taken entire teams weeks or months to produce. The "pattern-like" techniques and best practices contained in this book have been developed and refined over two years of onsite training with Excel users around the world, and the key lessons from those seminars costing thousands of dollars per day are now available within the pages of this easy-to-follow guide. This updated edition covers new features introduced with Office 2015.
Mastering Active Directory - Third Edition
Become an expert at managing enterprise identity infrastructure with Active Directory Domain Services 2022Key Features: Design and update your identity infrastructure by utilizing the latest Active Directory features and core capabilities.Secure your identity infrastructure from rising cybersecurity threats.Establish a strong identity foundation in the cloud by consolidating secure access. Book Description: Mastering Active Directory, Third Edition is a comprehensive guide for Information Technology professionals looking to improve their knowledge about MS Windows Active Directory Domain Service. The book will help you to use identity elements effectively and manage your organization's infrastructure in a secure and efficient way.This third edition has been fully updated to reflect the importance of cloud-based strong authentication and other tactics to protect identity infrastructure from emerging security threats. Mastering Active Directory provides extensive coverage of AD Domain Services and helps you explore their capabilities. This book will also teach you how to extend on-premises identity presence to cloud via Azure AD hybrid setup. By the end of this Microsoft Active Directory book, you'll feel confident in your ability to design, plan, deploy, protect, and troubleshoot your enterprise identity infrastructure.What You Will Learn: Install, protect, and manage Active Directory Domain Services (Windows Server 2022)Design your Hybrid Identity by evaluating business and technology requirementsAutomate administrative tasks in Active Directory using Windows PowerShell 7.xProtect sensitive data in a hybrid environment using Azure Information ProtectionLearn about Flexible Single Master Operation (FSMO) roles and their placementManage Directory Objects effectively using administrative tools and PowerShell.Centrally maintain state of user and computer configuration by using Group policies.Harden your Active Directory using security best practices.Who this book is for: If you are an Active Directory administrator, system administrator, or IT professional who has basic knowledge of Active Directory and is looking to become an expert in this topic, this book is for you.You need to have some experience of working with Active Directory to make the most of this book.
Pattern-Based Constraint Satisfaction and Logic Puzzles
"Pattern-Based Constraint Satisfaction and Logic Puzzles (Third Edition)" develops a pure logic, pattern-based perspective of solving the finite Constraint Satisfaction Problem (CSP), with emphasis on finding the "simplest" solution. Different ways of reasoning with the constraints are formalised by various families of "resolution rules", each of them carrying its own notion of simplicity. A large part of the book illustrates the power of the approach by applying it to various popular logic puzzles. It provides a unified view of how to model and solve them, even though they involve very different types of constraints: obvious symmetric ones in Sudoku, non-symmetric but transitive ones in Futoshiki, topological and geometric ones in Map colouring, Numbrix and Hidato, non-binary arithmetic ones in Kakuro and both non-binary and non-local ones in Slitherlink. It also shows that the most familiar techniques for these puzzles can be understood as mere application-specific presentations of the general rules. A free companion software (CSP-Rules-V2.1) implementing all the rules and above-mentioned applications is available on GitHub under the GPL license.
AWS Certified Cloud Practitioner Exam Guide
Develop proficiency in AWS technologies and validate your skills by becoming an AWS Certified Cloud PractitionerKey Features: Develop the skills to design highly available and fault-tolerant solutions in the cloudLearn how to adopt best-practice security measures in your cloud applicationsAchieve credibility through industry-recognized AWS Cloud Practitioner certification Book Description: Amazon Web Services is the largest cloud computing service provider in the world. Its foundational certification, AWS Certified Cloud Practitioner (CLF-C01), is the first step to fast-tracking your career in cloud computing. This certification will add value even to those in non-IT roles, including professionals from sales, legal, and finance who may be working with cloud computing or AWS projects. If you are a seasoned IT professional, this certification will make it easier for you to prepare for more technical certifications to progress up the AWS ladder and improve your career prospects.The book is divided into four parts. The first part focuses on the fundamentals of cloud computing and the AWS global infrastructure. The second part examines key AWS technology services, including compute, network, storage, and database services. The third part covers AWS security, the shared responsibility model, and several security tools. In the final part, you'll study the fundamentals of cloud economics and AWS pricing models and billing practices.Complete with exercises that highlight best practices for designing solutions, detailed use cases for each of the AWS services, quizzes, and two complete practice tests, this CLF-C01 exam study guide will help you gain the knowledge and hands-on experience necessary to ace the AWS Certified Cloud Practitioner exam.What You Will Learn: Create an AWS account to access AWS cloud services in a secure and isolated environmentUnderstand identity and access management (IAM), encryption, and multifactor authentication (MFA) protectionConfigure multifactor authentication for your IAM accountsConfigure AWS services such as EC2, ECS, Lambda, VPCs, and Route53Explore various storage and database services such as S3, EBS, and Amazon RDSStudy the fundamentals of modern application design to shift from a monolithic to microservices architectureDesign highly available solutions with decoupling ingrained in your design architectureWho this book is for: If you're looking to advance your career and gain expertise in cloud computing, with particular focus on the AWS platform, this book is for you. This guide will help you ace the AWS Certified Cloud Practitioner Certification exam, enabling you to embark on a rewarding career in cloud computing. No previous IT experience is essential to get started with this book, since it covers core IT fundamentals from the ground up.
Data Engineering with AWS
The missing expert-led manual for the AWS ecosystem - go from foundations to building data engineering pipelines effortlesslyPurchase of the print or Kindle book includes a free eBook in the PDF format.Key Features: Learn about common data architectures and modern approaches to generating value from big dataExplore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelinesLearn how to architect and implement data lakes and data lakehouses for big data analytics Book Description: Knowing how to architect and implement complex data pipelines is a highly sought-after skill. Data engineers are responsible for building these pipelines that ingest, transform, and join raw datasets - creating new value from the data in the process.Amazon Web Services (AWS) offers a range of tools to simplify a data engineer's job, making it the preferred platform for performing data engineering tasks.This book will take you through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. The book also teaches you about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data.By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.What You Will Learn: Understand data engineering concepts and emerging technologiesIngest streaming data with Amazon Kinesis Data FirehoseOptimize, denormalize, and join datasets with AWS Glue StudioUse Amazon S3 events to trigger a Lambda process to transform a fileRun complex SQL queries on data lake data using Amazon AthenaLoad data into a Redshift data warehouse and run queriesCreate a visualization of your data using Amazon QuickSightExtract sentiment data from a dataset using Amazon ComprehendWho this book is for: This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone who is new to data engineering and wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful.A basic understanding of big data-related topics and Python coding will help you get the most out of this book but is not needed. Familiarity with the AWS console and core services is also useful but not necessary.
Intelligent Automation Simplified
A guide to understand the potential of Intelligent Automation across businesses and enterprises.Key FeaturesA comprehensive discussion of key concepts, techniques, and key elements of intelligent automation.Expert coverage on combining various technologies, including RPA, AI, Blockchain, and IoT.Includes case studies and use cases for successful automation applications.Precise guidance on how to scale automation in enterprises.Description'Intelligent Automation Simplified' guides tech professionals to take a much more simplified and sophisticated step towards developing intelligent automation. This book will explain the basic concepts of smart automation and how to put it into practice for a company.This book explores each stage of automation design and explains how these automation fragments can be brought together in the end-to-end automation of workflow. This book discusses numerous examples and scenarios that will help relate and understand how technology can be used in real life to solve business problems. This book provides a lot of information and insights and helps readers grasp the methodology used to develop an automation solution correctly. With detailed illustrations and real use-cases, you will be able to easily create smart automation solutions and practice how to modify them.Towards the end, the book describes how smart automation expands in a company and discusses the various strategies for large-scale use. The book also highlights the latest trends in intelligent automation and its progress into the future of work.What you will learnLearn about the essential and primary components of intelligent automation.Investigate the capabilities of RPA and AI in the development of Intelligent Automation solutions.Recognize the factors that will help you choose the best processes for automation.Learn how to use the framework to create an Intelligent Automation solution.Create a blueprint to scale automation in the enterprise.Discover the most recent Intelligent Automation trends from industry experts.Who this book is forThis book is intended for current and future technical professionals who want to learn about Intelligent Automation, plan, and implement it in an enterprise or consult with clients. Readers should be familiar with the software development workflow and have a basic understanding of advanced technologies such as AI and RPA.Table of Contents1. Introduction to Intelligent Automation2. Robotic Process Automation3. Artificial Intelligence in Automation4. Other technologies in Automation5. Intelligent Automation Use cases6. Enterprise Automation Journey7. Intelligent Automation Trends and the futureRead more
Continuous Machine Learning with Kubeflow
An insightful journey to MLOps, DevOps, and Machine Learning in the real environment.Key FeaturesExtensive knowledge and concept explanation of Kubernetes components with examples.An all-in-one knowledge guide to train and deploy ML pipelines using Docker and Kubernetes.Includes numerous MLOps projects with access to proven frameworks and the use of deep learning concepts.Description'Continuous Machine Learning with Kubeflow' introduces you to the modern machine learning infrastructure, which includes Kubernetes and the Kubeflow architecture. This book will explain the fundamentals of deploying various AI/ML use cases with TensorFlow training and serving with Kubernetes and how Kubernetes can help with specific projects from start to finish.This book will help demonstrate how to use Kubeflow components, deploy them in GCP, and serve them in production using real-time data prediction. With Kubeflow KFserving, we'll look at serving techniques, build a computer vision-based user interface in streamlit, and then deploy it to the Google cloud platforms, Kubernetes and Heroku. Next, we also explore how to build Explainable AI for determining fairness and biasness with a What-if tool. Backed with various use-cases, we will learn how to put machine learning into production, including training and serving. After reading this book, you will be able to build your ML projects in the cloud using Kubeflow and the latest technology. In addition, you will gain a solid knowledge of DevOps and MLOps, which will open doors to various job roles in companies.What you will learnGet comfortable with the architecture and the orchestration of Kubernetes.Learn to containerize and deploy from scratch using Docker and Google Cloud Platform.Practice how to develop the Kubeflow Orchestrator pipeline for a TensorFlow model.Create AWS SageMaker pipelines, right from training to deployment in production.Build the TensorFlow Extended (TFX) pipeline for an NLP application using Tensorboard and TFMA.Who this book is forThis book is for MLOps, DevOps, Machine Learning Engineers, and Data Scientists who want to continuously deploy machine learning pipelines and manage them at scale using Kubernetes. The readers should have a strong background in machine learning and some knowledge of Kubernetes is required.Table of Contents1. Introduction to Kubeflow & Kubernetes Cloud Architecture2. Developing Kubeflow Pipeline in GCP3. Designing Computer Vision Model in Kubeflow4. Building TFX Pipeline5. ML Model Explainability & Interpretability 6. Building Weights & Biases Pipeline Development7. Applied ML with AWS Sagemaker8. Web App Development with Streamlit & HerokuRead more
The Future You
"AI is all around us. Self-driving cars. Smart personal assistants-think Siri, Cortana, or Google Now-or Alexa, Amazon's cloud-based voice service that is available on literally hundreds of millions of devices. Voice-to-text. Manufacturing robots. Facial recognition software. Security surveillance. Automated financial investing and social media monitoring. Smart homes that control themselves when their owners are out of town. The list is endless."All of the above make life easier for us. But in this new book by Moneyball Medicine author/podcaster Harry Glorikian, the spotlight is on how AI can (and will, and in many cases already does) make us healthier."-from the Foreword to The Future You by Dr. Bob ArnotDo you have a smartphone and a wearable device, such as an Apple Watch or a Fitbit? Most likely yes, right? Well, then, as Glorikian tells us, there are already numerous apps available for download that " ... can also continuously monitor temperature, calorie intake, blood glucose, menstruation cycle, respiration rate, stress levels, brain waves, or just about any other aspect of physical and mental health you want." They identify areas where improvement is needed, and tell us how to improve our health in those areas.
The Future You
"AI is all around us. Self-driving cars. Smart personal assistants-think Siri, Cortana, or Google Now-or Alexa, Amazon's cloud-based voice service that is available on literally hundreds of millions of devices. Voice-to-text. Manufacturing robots. Facial recognition software. Security surveillance. Automated financial investing and social media monitoring. Smart homes that control themselves when their owners are out of town. The list is endless."All of the above make life easier for us. But in this new book by Moneyball Medicine author/podcaster Harry Glorikian, the spotlight is on how AI can (and will, and in many cases already does) make us healthier."-from the Foreword to The Future You by Dr. Bob ArnotDo you have a smartphone and a wearable device, such as an Apple Watch or a Fitbit? Most likely yes, right? Well, then, as Glorikian tells us, there are already numerous apps available for download that " ... can also continuously monitor temperature, calorie intake, blood glucose, menstruation cycle, respiration rate, stress levels, brain waves, or just about any other aspect of physical and mental health you want." They identify areas where improvement is needed, and tell us how to improve our health in those areas.
The Data-Confident Internal Auditor
For internal auditors, developing trends in data analysis and data science can feel less like a wealth of information and more like an avalanche. Still, better use of data provides an opportunity to advance your career by adopting new, invaluable skills. The missing link? Jargon-free guidance that cuts through the hype. The Data-Confident Internal Auditor demystifies the use of data in internal audits through practical, step-by-step guidance. With concepts and tools that are easy to understand and apply, this comprehensive guide shows you how to approach data yourself, without having to wait on data scientists or technical experts. Developed over the course of hundreds of actual audits, these real-world approaches and practices are distilled into a simple sequence of steps that will leave you feeling confident and even eager to apply them for yourself. Pick up The Data-Confident Internal Auditor and start building your data skills today.
AI-Powered Commerce
Learn how to use artificial intelligence for product and service innovation, including the diverse use cases of Commerce.AIKey Features: Learn how to integrate data and AI in your innovation workflowsUnlock insights into how various industries are using AI for innovationApply your knowledge to real innovation use cases like product strategy and market intelligence Book Description: Commerce.AI is a suite of artificial intelligence (AI) tools, trained on over a trillion data points, to help businesses build next-gen products and services. If you want to be the best business on the block, using AI is a must.Developers and analysts working with AI will be able to put their knowledge to work with this practical guide. You'll begin by learning the core themes of new product and service innovation, including how to identify market opportunities, come up with ideas, and predict trends. With plenty of use cases as reference, you'll learn how to apply AI for innovation, both programmatically and with Commerce.AI. You'll also find out how to analyze product and service data with tools such as GPT-J, Python pandas, Prophet, and TextBlob. As you progress, you'll explore the evolution of commerce in AI, including how top businesses today are using AI. You'll learn how Commerce.AI merges machine learning, product expertise, and big data to help businesses make more accurate decisions. Finally, you'll use the Commerce.AI suite for product ideation and analyzing market trends.By the end of this artificial intelligence book, you'll be able to strategize new product opportunities by using AI, and also have an understanding of how to use Commerce.AI for product ideation, trend analysis, and predictions.What You Will Learn: Find out how machine learning can help you identify new market opportunitiesUnderstand how to use consumer data to create new products and servicesUse state-of-the-art AI frameworks and tools for data analysisLaunch, track, and improve products and services with AIRise above the competition with unparalleled insights from AITurn customer touchpoints into business winsGenerate high-conversion product and service copyWho this book is for: This AI book is for AI developers, data scientists, data product managers, analysts, and consumer insights professionals. The book will guide you through the process of product and service innovation, no matter your pre-existing skillset.
Pattern-Based Constraint Satisfaction and Logic Puzzles (Third Edition)
"Pattern-Based Constraint Satisfaction and Logic Puzzles (Third Edition)" develops a pure logic, pattern-based perspective of solving the finite Constraint Satisfaction Problem (CSP), with emphasis on finding the "simplest" solution. Different ways of reasoning with the constraints are formalised by various families of "resolution rules", each of them carrying its own notion of simplicity. A large part of the book illustrates the power of the approach by applying it to various popular logic puzzles. It provides a unified view of how to model and solve them, even though they involve very different types of constraints: obvious symmetric ones in Sudoku, non-symmetric but transitive ones in Futoshiki, topological and geometric ones in Map colouring, Numbrix and Hidato, non-binary arithmetic ones in Kakuro and both non-binary and non-local ones in Slitherlink. It also shows that the most familiar techniques for these puzzles can be understood as mere application-specific presentations of the general rules. A free companion software (CSP-Rules-V2.1) implementing all the rules and above-mentioned applications is available on GitHub under the GPL license.
Strategic Monoliths and Microservices
Make Software Architecture Choices That Maximize Value and Innovation "[Vernon and Jaskula] provide insights, tools, proven best practices, and architecture styles both from the business and engineering viewpoint. . . . This book deserves to become a must-read for practicing software engineers, executives as well as senior managers." --Michael Stal, Certified Senior Software Architect, Siemens Technology Strategic Monoliths and Microservices helps business decision-makers and technical team members clearly understand their strategic problems through collaboration and identify optimal architectural approaches, whether the approach is distributed microservices, well-modularized monoliths, or coarser-grained services partway between the two. Leading software architecture experts Vaughn Vernon and Tomasz Jaskula show how to make balanced architectural decisions based on need and purpose, rather than hype, so you can promote value and innovation, deliver more evolvable systems, and avoid costly mistakes. Using realistic examples, they show how to construct well-designed monoliths that are maintainable and extensible, and how to gradually redesign and reimplement even the most tangled legacy systems into truly effective microservices. Link software architecture planning to business innovation and digital transformation Overcome communication problems to promote experimentation and discovery-based innovation Master practices that support your value-generating goals and help you invest more strategically Compare architectural styles that can lead to versatile, adaptable applications and services Recognize when monoliths are your best option and how best to architect, design, and implement them Learn when to move monoliths to microservices and how to do it, whether they're modularized or a "Big Ball of Mud" Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Towards Autonomous Robotic Systems
The volume LNAI 13054 constitutes the refereed proceedings of the 22th Annual Conference Towards Autonomous Robotic Systems, TAROS 2021, held in Lincoln, UK, in September 2021.*The 45 full papers were carefully reviewed and selected from 66 submissions. Organized in the topical sections "Algorithms" and "Systems", they discuss significant findings and advances in the following areas: artificial intelligence; mechatronics; image processing and computer vision; special purpose and application-based systems; user interfaces and human computer interaction.* The conference was held virtually due to the COVID-19 pandemic.
Machine Learning for Time-Series with Python
Become proficient in deriving insights from time-series data and analyzing a model's performanceKey Features: Explore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time-series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description: Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.What You Will Learn: Understand the main classes of time-series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models like Gaussian processes and transformers and state-of-the-art machine learning modelsBecome familiar with many libraries like prophet, xgboost, and TensorFlowWho this book is for: This book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.
Conquering R Basics
This book and its accompanying video library attempts to tackle a colossal challenge. It proposes to define what is otherwise an ambiguous interpretation of a basic skill set underlying a global technology. That technology is R. The R basic skill set proposed in this book comprises the following targeted elements: (93) indispensable R functions are identified and explained, all of which can be used in a vast array of data projects.The concept of the Data Narrative is introduced and explained.Data is programmatically connected with commonly used plot visualizations, explained with specificity and supporting context.The application of color in plot development is introduced and explained.
Aprendizaje Autom獺tico en Acci籀n
聶Est獺 buscando un libro fundamental para comenzar con los conceptos b獺sicos del aprendizaje autom獺tico? Mi libro le explicar獺 los conceptos b獺sicos de manera que sean f獺ciles de entender. Una vez que haya le穩do este libro, tendr獺 una s籀lida comprensi籀n de los principios b獺sicos que facilitar獺n el paso a un libro m獺s avanzado si desea obtener m獺s informaci籀n.
Semi-Custom IC Design and VLSI
The contents of this book were first presented as a series of lectures at the first IEE Vacation School on Semi-Custom IC Design and VLSI held at the University of Edinburgh on 4-8 July 1983. The earlier chapters provide an introduction to silicon IC technology and include descriptions of the various processing techniques employed in the manufacture of microelectronic components. Different types of semi-custom IC are then reviewed and the factors that have to be considered in choosing a semi-custom technique are examined in detail. Logic design is next presented as an activity that is best carried out at a higher level of abstraction than the customary/logic gate level by using the algorithmic state machine (ASM) method. In the sections that follow, computer aids to design and design automation tools are introduced as essential requirements for the rapid and error-free design of semicustom ICs. Testing strategies and the need to design for testability are also covered in some detail. Although a heavy emphasis is placed on the design of semi-custom ICs, consideration is also given to the ways in which custom VLSI circuits will be designed in future. The merits of the programmable logic array (PLA) as a VLSI building-block are put forward, and the silicon compiler is presented as possibly the ultimate 'semi-custom' technique. The authors who have contributed to this volume are specialists in their field who can claim many years of experience either in the microelectronics industry or in universities throughout the UK.
Simplifying Service Management with Consul
Understand the basics of the Consul server and client architecture, and learn how to apply Consul for dynamic and secure service discovery, communication, and network connectivity automationKey Features: Discover how Consul servers and clients operate to facilitate primary Consul use casesLearn how Consul dynamically and securely discovers and shares service data throughout the networkUtilize Consul to extend and secure network communications across multiple operating environments Book Description: Within the elastic and dynamic nature of cloud computing, efficient and accurate service discovery provides the cornerstone for all communications. HashiCorp Consul facilitates this service discovery efficiently and securely, independent of the operating environment. This book will help you build a solid understanding of both the concepts and applications of HashiCorp Consul.You'll begin by finding out what you can do with Consul, focusing on the conceptual views of configuration samples along with Terraform code to expedite lab environment and hands-on experimentation, which will enable you to apply Consul effectively in your everyday lives. As you advance, you'll learn how to set up your own Consul cluster and agents in a single datacenter or location and understand how Consul utilizes RAFT and GOSSIP protocols for communication. You'll also explore the practical applications of primary Consul use cases, including communication flows and configuration and code examples. With that knowledge, you'll extend Consul across datacenters to discuss the applicability of multiple regions, multiple clouds, and hybrid cloud environments.By the end of this Consul book, you will have the tools needed to create and operate your own Consul cluster and be able to facilitate your service discovery and communication.What You Will Learn: Deploy and configure a highly available multi-node Consul architectureImplement Consul service discovery across multiple servicesUtilize Consul to monitor and communicate service health statusConnect services securely across a range of environmentsLeverage your knowledge of the Consul service to automate network infrastructureExtend your Consul knowledge and connectivity across multiple environmentsWho this book is for: If you are a solutions architect, DevOps engineer, or anyone new to the cloud-native framework looking to get started with using Consul, then this book is for you. Knowledge of Terraform is helpful but not necessary. A basic understanding of networking and Kubernetes systems will help you get the most out of this book.
Serverless Analytics with Amazon Athena
Get more from your data with Amazon Athena's ease-of-use, interactive performance, and pay-per-query pricingKey Features: Explore the promising capabilities of Amazon Athena and Athena's Query Federation SDKUse Athena to prepare data for common machine learning activitiesCover best practices for setting up connectivity between your application and Athena and security considerationsBook Description: Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using SQL, without needing to manage any infrastructure.This book begins with an overview of the serverless analytics experience offered by Athena and teaches you how to build and tune an S3 Data Lake using Athena, including how to structure your tables using open-source file formats like Parquet. You'll learn how to build, secure, and connect to a data lake with Athena and Lake Formation. Next, you'll cover key tasks such as ad hoc data analysis, working with ETL pipelines, monitoring and alerting KPI breaches using CloudWatch Metrics, running customizable connectors with AWS Lambda, and more. Moving on, you'll work through easy integrations, troubleshooting and tuning common Athena issues, and the most common reasons for query failure. You will also review tips to help diagnose and correct failing queries in your pursuit of operational excellence. Finally, you'll explore advanced concepts such as Athena Query Federation and Athena ML to generate powerful insights without needing to touch a single server.By the end of this book, you'll be able to build and use a data lake with Amazon Athena to add data-driven features to your app and perform the kind of ad hoc data analysis that often precedes many of today's ML modeling exercises.What You Will Learn: Secure and manage the cost of querying your dataUse Athena ML and User Defined Functions (UDFs) to add advanced features to your reportsWrite your own Athena Connector to integrate with a custom data sourceDiscover your datasets on S3 using AWS Glue CrawlersIntegrate Amazon Athena into your applicationsSetup Identity and Access Management (IAM) policies to limit access to tables and databases in Glue Data CatalogAdd an Amazon SageMaker Notebook to your Athena queriesGet to grips with using Athena for ETL pipelinesWho this book is for: Business intelligence (BI) analysts, application developers, and system administrators who are looking to generate insights from an ever-growing sea of data while controlling costs and limiting operational burden, will find this book helpful. Basic SQL knowledge is expected to make the most out of this book.
Modern Problems of Robotics
This book constitutes the post-conference proceedings of the 2nd International Conference on Modern Problems of Robotics, MPoR 2020, held in Moscow, Russia, in March 2020.The 16 revised full papers were carefully reviewed and selected from 21 submissions. The volume includes the following topical sections: Collaborative Robotic Systems, Robotic Systems Design and Simulation, and Robots Control. The papers are devoted to the most interesting today's investigations in Robotics, such as the problems of the human-robot interaction, the problems of robot design and simulation, and the problems of robot and robotic complexes control.
A Greater Foundation for Machine Learning Engineering
This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Na簿ve Bayes, Na簿ve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.
