Take a look at our Information Technology books. Shulph carries a great selection of Information Technology books, and we are always adding more.
Learn how to automate tasks and create rules in Jira with the help of different use casesKey FeaturesAutomate daily repetitive and tedious tasks without coding experienceDiscover how to automate processes in the Jira family including Jira software, Jira Service Desk, and Jira CoreExplore different use cases to understand automation features in JiraBook DescriptionAtlassian Jira makes it easier to track the progress of your projects, but it can lead to repetitive and time-consuming tasks for teams. No-code automation will enable you to increase productivity by automating these tasks. Automate Everyday Tasks in Jira provides a hands-on approach to implementation and associated methodologies that will have you up and running and productive in no time. You will start by learning how automation in Jira works, along with discovering best practices for writing automation rules. Then you'll be introduced to the building blocks of automation, including triggers, conditions, and actions, before moving on to advanced rule-related techniques. After you've become familiar with the techniques, you'll find out how to integrate with external tools, such as GitHub, Slack, and Microsoft Teams, all without writing a single line of code. Toward the end, you'll also be able to employ advanced rules to create custom notifications and integrate with external systems. By the end of this Jira book, you'll have gained a thorough understanding of automation rules and learned how to use them to automate everyday tasks in Jira without using any code.What you will learnUnderstand the basic concepts of automation such as triggers, conditions, and actionsFind out how to use if–then scenarios and conditions to automate your processes with practical examplesUse smart values to achieve complex and more powerful automationImplement use cases in a practical way, including automation with Slack, Microsoft Teams, GitHub, and BitbucketDiscover best practices for writing and maintaining automation rulesExplore techniques for debugging rules and solving common issuesWho this book is forThis book is for Jira administrators and project managers who want to learn about automation capabilities provided in Jira. Familiarity with Jira and working knowledge of workflows and project configurations is required.
Understand, design, and create cognitive applications using Watson's suite of APIs. Key Features Develop your skills and work with IBM Watson APIs to build efficient and powerful cognitive apps Learn how to build smart apps to carry out different sets of activities using real-world use cases Get well versed with the best practices of IBM Watson and implement them in your daily work Book Description Cognitive computing is rapidly infusing every aspect of our lives riding on three important fields: data science, machine learning (ML), and artificial intelligence (AI). It allows computing systems to learn and keep on improving as the amount of data in the system grows. This book introduces readers to a whole new paradigm of computing – a paradigm that is totally different from the conventional computing of the Information Age. You will learn the concepts of ML, deep learning (DL), neural networks, and AI through the set of APIs provided by IBM Watson. This book will help you build your own applications to understand, plan, and solve problems, and analyze them as per your needs. You will learn about various domains of cognitive computing, such as NLP, voice processing, computer vision, emotion analytics, and conversational systems, using different IBM Watson APIs. From this, the reader will learn what ML is, and what goes on in the background to make computers "do their magic," as well as where these concepts have been applied. Having achieved this, the readers will then be able to embark on their journey of learning, researching, and applying the concept in their respective fields. What you will learn Get well versed with the APIs provided by IBM Watson on IBM Cloud Learn ML, AI, cognitive computing, and neural network principles Implement smart applications in fields such as healthcare, entertainment, security, and more Understand unstructured content using cognitive metadata with the help of Natural Language Understanding Use Watson's APIs to create real-life applications to realize their capabilities Delve into various domains of cognitive computing, such as media analytics, embedded deep learning, computer vision, and more Who this book is for This book is for beginners and novices; having some knowledge about artificial intelligence and deep learning is an advantage, but not a prerequisite to benefit from this book. We explain the concept of deep learning and artificial intelligence through the set of tools IBM Watson provides.
Your perfect companion to prepare for and pass the CompTIA Project+ PK0-004 exam Key Features Manage project changes and deliver desired project outcomes Gain confidence in passing the PK0-004 exam with the help of practice questions Obtain insight from J. Ashley Hunt, an accomplished subject matter expert Book Description The CompTIA Project+ exam is designed for IT professionals who want to improve their career trajectory by gaining certification in project management specific to their industry. This guide covers everything necessary to pass the current iteration of the Project+ PK0-004 exam. The CompTIA Project+ Certification Guide starts by covering project initiation best practices, including an understanding of organizational structures, team roles, and responsibilities. You'll then study best practices for developing a project charter and the scope of work to produce deliverables necessary to obtain formal approval of the end result. The ability to monitor your project work and make changes as necessary to bring performance back in line with the plan is the difference between a successful and unsuccessful project. The concluding chapters of the book provide best practices to help keep an eye on your projects and close them out successfully. The guide also includes practice questions created to mirror the exam experience and help solidify your understanding of core project management concepts. By the end of this book, you will be able to develop creative solutions for complex issues faced in project management. What you will learn Develop a project charter and define team roles and responsibilities Plan the project scope, schedule, budget, and risks Process change requests and work with procurement documents Close a formal project or phase and get an overview of Agile Project Management principles Create a work breakdown structure (WBS) and dictionary Discover best practices for identifying, analyzing, and responding to risk Gain important exam information and discover the next steps Who this book is for The CompTIA Project+ Certification Guide is for entry-level project managers who are looking for a common language and best practices in the IT project management space as well as a certification to excel in their career.
This volume serves to recognize the uniqueness of the moment; the number of new users of e-services worldwide will double during 2015-2018 (moving from 2 billion users mostly living in the developed nations to an additional 2 billion users mostly living in developing nations). This radical embrace of new e-service technologies will substantially improve the quality of lives for most residents globally. A profound happening occurring now! The new technologies combine rapidly delivering of a multitude of services at extremely low cost to adopters now having extremely low incomes relative to residents living in developed nations. Adoption of e-service among residents in developing nations ends the debate as to whether or not marketing to the "bottom of the pyramid" is possible. The more relevant issues focus on describing and explaining e-service adoption processes in developing nations. How are these processes being implemented? What obstacles had to be overcome in achieving these adoptions? How were these obstacles overcome? Read this volume for research providing useful answers to these questions.
Volume 23B includes two chapters covering problems and implementations of solutions in e-services adoption processes in developing nations. The first documents the unequal access and ICT usage, which is known as digital divide, to be one of the major obstacles to the implementation of e-government systems. This research investigates the digital divide and its direct impact on e-government system success of local governments in Indonesia as well as indirect impact through the mediation role of trust. To achieve a comprehensive understanding of digital divide, this study introduced a new type of digital divide, the innovativeness divide. It provides details for successful policy formulation to improve e-government readiness. The second explores what needs to be done to enable consumers to adopt e-services by airlines in developing nations. It includes new theory and empirical evidence from both qualitative and quantitative studies in response to this issue. Exciting and useful chapters for executives and researchers seeking knowledge and theory of how to influence e-service adoptions in developing nations!
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 FeaturesLearn 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 DescriptionThe 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 learnConfigure 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 forDevelopers 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.
Combine popular machine learning techniques to create ensemble models using Python Key Features Implement ensemble models using algorithms such as random forests and AdaBoost Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and Keras Book Description Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. What you will learn Implement ensemble methods to generate models with high accuracy Overcome challenges such as bias and variance Explore machine learning algorithms to evaluate model performance Understand how to construct, evaluate, and apply ensemble models Analyze tweets in real time using Twitter's streaming API Use Keras to build an ensemble of neural networks for the MovieLens dataset Who this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.
Implement modern DevOps techniques to increase business productivity, agility, reliability, security, and scalabilityKey FeaturesLearn how to use business resources effectively for improved productivity and collaborationUse infrastructure as code practices to build large-scale cloud infrastructureLeverage the ultimate open source DevOps tools to achieve continuous integration and continuous delivery (CI/CD)Book DescriptionIn the implementation of DevOps processes, the choice of tools is crucial to the sustainability of projects and collaboration between developers and ops. This book presents the different patterns and tools for provisioning and configuring an infrastructure in the cloud, covering mostly open source tools with a large community contribution, such as Terraform, Ansible, and Packer, which are assets for automation.This DevOps book will show you how to containerize your applications with Docker and Kubernetes and walk you through the construction of DevOps pipelines in Jenkins as well as Azure pipelines before covering the tools and importance of testing. You'll find a complete chapter on DevOps practices and tooling for open source projects before getting to grips with security integration in DevOps using Inspec, Hashicorp Vault, and Azure Secure DevOps kit. You'll also learn about the reduction of downtime with blue-green deployment and feature flags techniques before finally covering common DevOps best practices for all your projects.By the end of this book, you'll have built a solid foundation in DevOps and developed the skills necessary to enhance a traditional software delivery process using modern software delivery tools and techniques.What you will learnUnderstand the basics of infrastructure as code patterns and practicesGet an overview of Git command and Git flowInstall and write Packer, Terraform, and Ansible code for provisioning and configuring cloud infrastructure based on Azure examplesUse Vagrant to create a local development environmentContainerize applications with Docker and KubernetesApply DevSecOps for testing compliance and securing DevOps infrastructureBuild DevOps CI/CD pipelines with Jenkins, Azure Pipelines, and GitLab CIExplore blue-green deployment and DevOps practices for open sources projectsWho this book is forIf you are an application developer or a system administrator interested in understanding continuous integration, continuous delivery, and containerization with DevOps tools and techniques, this book is for you. Knowledge of DevOps fundamentals and Git principles is required.
Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries Key Features Your entry point into the world of artificial intelligence using the power of Python An example-rich guide to master various RL and DRL algorithms Explore the power of modern Python libraries to gain confidence in building self-trained applications Book Description Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL. By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems. This Learning Path includes content from the following Packt products: Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani What you will learn Train an agent to walk using OpenAI Gym and TensorFlow Solve multi-armed-bandit problems using various algorithms Build intelligent agents using the DRQN algorithm to play the Doom game Teach your agent to play Connect4 using AlphaGo Zero Defeat Atari arcade games using the value iteration method Discover how to deal with discrete and continuous action spaces in various environments Who this book is for If you're an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.
In today’s data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled with the help of traditional techniques. Self-learning and adaptive algorithms are now widely used by such leading giants that as Google, Tesla, Microsoft, and Facebook in their projects and applications. In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear. Including research relevant to those studying cybernetics, applied mathematics, statistics, engineering, and bioinformatics who are working in the areas of machine learning, artificial intelligence, complex system modeling and analysis, neural networks, and optimization, this is an ideal read for anyone interested in learning more about the fascinating new developments in machine learning.