Название: Salesforce Developer I Certification: Learn the Basics of Apex, Lightning Web Components, and Flow
Автор: Konstantin Kapitanov
Издательство: Apress
Год: 2024
Страниц: 201
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

This book is a comprehensive learning guide tailored for professionals who primarily work as non-coders within the Salesforce ecosystem. By providing clear and practical insights, it serves as a roadmap to inspire individuals, regardless of their coding background, to embark on a systematic journey towards acquiring programming skills while navigating the Salesforce platform. This understanding of programming fundamentals is not only valuable for professional growth but also to cover exam preparation for certification as a Salesforce Developer I. Because it includes materials and practical code examples specifically tailored to the exam preparation. In addition the book equips readers with the practical knowledge and confidence to leverage potential of Salesforce's capabilities. This book covers a range of important topics, including Salesforce platform fundamentals and architecture, automatization tools, Apex as a programming language with code examples like triggers, methods, SOQL, SOSL, testing, Lightning web components, deployment tools, and various other subjects, including declarative programming methods by using Flow. Many of these topics are interconnected with relevant code examples or explanations tailored for the Developer I exam as well. Apex is a programming language developed by Salesforce specifically for building applications on the Salesforce platform. It shares similarities with Java in terms of syntax and structure, as it’s influenced by Java. It is a strongly typed, case-insensitive, object-oriented language. As operate on multi-tenant environment, you can save your code against different API versions.

Название: Practical GitLab Services: A Complete DevOps Guide for Developers and Administrators
Автор: Jeffrey Painter
Издательство: Apress
Год: 2024
Страниц: 909
Язык: английский
Формат: pdf
Размер: 65.2 MB

Learn all about GitLab administration, the open-source DevOps platform helping millions develop and deploy safe and secure software. This bookis designed to be your one-stop shop to get up and running with complete ease. Start with the basics like setting up a GitLab account and exploring user options before moving on to GitLab’s primary function as a source code management tool. From there, you’ll learn about one of the most unique features of GitLab: CI/CD configuration files. The next step on the journey is a discussion of how to organize code into projects and groups, along with defining access permissions using roles. You’ll also examine the various planning tools provided by GitLab. With the basic features out of the way, you’ll delve into more specialized services such as the built-in Docker image repository, the package registry, web page creation, and binary file storage. The book concludes with more advanced administrative topics such as working with GitLab’s REST and GraphQL APIs, setting up a proof-of-concept GitLab service using Amazon Web Services (AWS), and spinning up a production service that takes high availability and disaster recovery into account. With an abundance of information available for deploying secure software, selecting the right documentation can often be daunting. From planning to monitoring, Practical GitLab Services eliminates the unknown in your DevOps lifecycle. For software developers, potential GitLab administrators, supporting team members such as testers and project managers.

Название: Unlocking Artificial Intelligence: From Theory to Applications
Автор: Christopher Mutschler, Christian Münzenmayer, Norman Uhlmann, Alexander Martin
Издательство: Springer
Год: 2024
Страниц: 382
Язык: английский
Формат: pdf (true)
Размер: 39.1 MB

This book provides a state-of-the-art overview of current Machine Learning research and its exploitation in various application areas. It has become apparent that the deep integration of Artificial Intelligence (AI) methods in products and services is essential for companies to stay competitive. The use of AI allows large volumes of data to be analyzed, patterns and trends to be identified, and well-founded decisions to be made on an informative basis. It also enables the optimization of workflows, the automation of processes and the development of new services, thus creating potential for new business models and significant competitive advantages. The book is divided in two main parts: First, in a theoretically oriented part, various AI/ML-related approaches like automated Machine Learning, sequence-based learning, Deep Learning, learning from experience and data, and process-aware learning are explained. In a second part, various applications are presented that benefit from the exploitation of recent research results. These include autonomous systems, indoor localization, medical applications, energy supply and networks, logistics networks, traffic control, image processing, and IoT applications. In the past few years Automated Machine Learning (AutoML) has gained a lot of traction in the Data Science and Machine Learning community. AutoML aims at reducing the partly repetitive work of data scientists and enabling domain experts to construct Machine Learning pipelines without extensive knowledge in Data Science. The Chapter 1 presents a comprehensive review of the current leading AutoML methods and sets AutoML in an industrial context. To this extent we present the typical components of an AutoML system, give an overview over the state-of-the-art and highlight challenges to industrial application by presenting several important topics such as AutoML for time series data, AutoML in unsupervised settings, AutoML with multiple evaluation criteria, or interactive human-in-the-loop methods.

Название: Deep Learning Concepts in Operations Research
Автор: Biswadip Basu Mallik, Gunjan Mukherjee, Rahul Kar, Aryan Chaudhary
Издательство: CRC Press
Серия: Advances in Computational Collective Intelligence
Год: 2025
Страниц: 277
Язык: английский
Формат: pdf (true)
Размер: 29.2 MB

The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the Machine Learning (ML) paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the Machine Learning model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of Artificial Intelligence (AI) and ML as well. Deep Learning is derived from traditional neural networks, but is much more efficient than its predecessors. Also, Deep Learning uses both transformations and graphs to create multi-​layered learning models. Recently developed DL techniques include sound and discourse handling, visual information preparation, common dialect handling (NLP), and the like. Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by Artificial Intelligence and Machine Learning. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost.

Название: Business Analytics with R and Python
Автор: David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Издательство: Springer
Серия: AI for Risks
Год: 2024
Страниц: 201
Язык: английский
Формат: pdf (true), epub
Размер: 42.5 MB

This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or Artificial Intelligence (AI) to provide forecasting capability. R is a command line software. R Studio, a Graphical User Interface (GUI) and integrated programming environment developed for R, provides a user-friendly programming environment by making screen menus for packages, functions, and files, surfing and saving utilities, and concurrent access to multiple scripts, files, and directories. R packages provide pre-programmed functions for a variety of analytical processes. Python is also a command line programming language. Python base is a limited library, available to download with a standard command line editor. Python is available for various operating systems like Windows, macOS, and Linux. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of Artificial Intelligence.

Название: Prototyping Python Dashboards for Scientists and Engineers: Build and Deploy a Complete Dashboard with Python
Автор: Padraig Houlahan
Издательство: Apress
Год: 2024
Страниц: 220
Язык: английский
Формат: pdf (true), epub (true)
Размер: 22.2 MB

Create interactive and data-driven dashboards using Python. This hands-on guide is a practical resource for those (with modest programming skills) in scientific and engineering fields looking to leverage Python's power for data visualization and analysis in a user-friendly dashboard format. You’ll begin by gaining a fundamental understanding of Python programming, including data types, lists, dictionaries, and data structures. The book then delves into the world of reactive programming with Plotly and Dash, offering a hands-on approach to building interactive web-based dashboards. Next, you’ll see how to work with online data, how to scrape and clean datasets, and keep files up-to-date. The book also guides you through planning a dashboard prototype, outlining project tasks, trends, forecasts, spectra, and other design considerations. It concludes with a discussion of how the dashboard can be used for data visualization of real data, explaining the usefulness of tools such as spectra. For scientists, engineers, students, programmers, and data enthusiasts who aspire to harness Python's potential for data visualization and analysis through the creation of interactive dashboards.

Название: How To Python
Автор: Ben Good
Издательство: Independently published
Год: 2024
Страниц: 104
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

Embark on your coding journey with Ben Good’s transformative book, "How To Python". Ideal for novices and those looking to refresh their skills, this guide breaks down Python programming into digestible, engaging lessons packed with real-world examples. Each section builds on the previous, from simple scripts to complex algorithms, ensuring a solid foundation in Python’s dynamic capabilities. Ben Good empowers you to craft, debug, and deploy your applications, pushing the boundaries of what you can achieve with code. Step into the realm of programming with confidence and creativity with this essential Python manual! “How to Python” - a comprehensive guide designed to illuminate the path for those embarking on their journey through the dynamic and expansive world of Python programming. Whether you’re a complete novice looking to make your first foray into coding, or an experienced developer seeking to deepen your expertise in Python, this book aims to equip you with the knowledge and tools necessary to excel in this versatile language. Python, known for its simplicity yet powerful capabilities, sits at the forefront of innovation in software development. It serves as the backbone of numerous modern technologies, including web development, data analysis, Artificial Intelligence, and more. This versatility is one of the reasons Python has become one of the most popular programming languages in the world today. In writing this book, we’ve meticulously crafted each chapter to build upon the last, ensuring a structured learning experience that not only teaches you how to code in Python but also how to think like a Python programmer. From setting up your development environment and writing your first Python script to delving into advanced topics like Machine Learning and cloud integration, “How to Python” covers a broad spectrum of practical and engaging content.

Название: Ada Programming: Reliable, Strongly-Typed Systems Programming
Автор: Theophilus Edet
Издательство: CompreQuest Books
Год: 2024
Страниц: 769
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Ada Programming: Reliable, Strongly-Typed Systems Programming offers an in-depth exploration of Ada, a language renowned for its reliability, robustness, and versatility in systems programming. Whether you're a seasoned developer or just beginning your journey, this comprehensive guide will help you master Ada and its unique features through a comprehensive set of essential constructs of Variables, Functions, Conditions, Collections, Loops, Comments, Enums, Classes, Accessors, and Scope, as well as the Ada's strong support for implementations of Concurrent Programming, Object-Oriented Programming (OOP), Procedural Programming, Security-Oriented Programming (SOP), and Structured Programming. Ada stands out in the programming world due to its strong typing, modularity, and exceptional error-checking capabilities. Designed for reliability and efficiency, Ada is the go-to language for critical systems where safety and security are paramount. This book delves into these essential features, providing a solid foundation for developing reliable and maintainable software systems. Learn about Ada’s powerful type system, modular programming capabilities, and robust error-handling mechanisms, all of which are crucial for creating software that is both reliable and maintainable. The journey begins with an introduction to the basic constructs of Ada, providing readers with a solid foundation in the language's syntax and semantics. We explore fundamental concepts such as data types, control structures, and subprograms, emphasizing Ada’s strict type checking and error prevention mechanisms. Through clear explanations and practical examples, readers will gain confidence in writing correct and efficient Ada code from the outset. Building on this foundation, the book transitions into more advanced topics, including object-oriented programming, generic programming, and tasking. These chapters highlight Ada’s unique approach to these paradigms, demonstrating how its features can be used to create modular, reusable, and concurrent systems. By understanding these advanced concepts, readers will be equipped to tackle complex software challenges with Ada’s robust toolkit.

Название: Artificial Intelligence: A Guide for Everyone
Автор: Arshad Khan
Издательство: Springer
Год: 2024
Страниц: 278
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Enterprises, as well as individuals, are racing to reap the benefits of Artificial Intelligence (AI). However, in most cases, they are doing so without understanding the technology or its implications and risks, which can be significant. Artificial Intelligence: A Guide for Everyone is a step in addressing that gap by providing information that readers can easily understand at every level. This book aims to provide useful information to those planning, developing, or using AI, which has the potential to transform industries and shape the future. Artificial Intelligence: A Guide for Everyone encompasses theoretical as well as practical aspects of AI across various industries and applications. It demystifies AI by explaining, in a language that non-techies can follow, its history, different types, differentiating technologies, and various aspects of implementation. It explains the connection between AI theory and real-world application across diverse industries and how it fuels innovation. Whether you are a simple business user, a professional, a tech enthusiast, a manager, an executive, or an aspiring AI developer, this guide is your roadmap to navigate the intricacies of AI. The topics covered in this book are comprehensive and diverse. This guide explains the prerequisites for building, developing, and deploying an AI system. It discusses its key techniques, technologies, and components, such as Machine Learning, natural language processing, and Deep Learning. It explains the process of constructing an AI system, offering insights into problem definition, data preprocessing, model development, evaluation, testing, deployment, and monitoring. In consideration of practical resource limitations and constraints, pre-built, ready-to-use AI solutions, which democratize AI, are discussed. The journey concludes by categorizing AI based on intelligence levels and functionality, providing a comprehensive understanding of Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI), and other categories.

Название: Geophysical Data Analysis and Inverse Theory with MATLAB and Python, 5th Edition
Автор: William Menke
Издательство: Academic Press, Elsevier
Год: 2024
Страниц: 344
Язык: английский
Формат: pdf (true)
Размер: 29.7 MB

Geophysical Data Analysis and Inverse Theory with MATLAB or Python, Fifth Edition is a revised and expanded introduction to inverse theory and tomography as it is practiced by geophysicists. The book demonstrates the methods needed to analyze a broad spectrum of geophysical datasets, with special attention given to those methods that generate images of the earth. Data analysis can be a mathematically complex activity, but the treatment in this volume is carefully designed to emphasize those mathematical techniques that readers will find the most familiar and to systematically introduce less-familiar ones. A series of "crib sheets" offer step-by-step summaries of methods presented. Utilizing problems and case studies, along with MATLAB and Python computer code and summaries of methods, the book provides professional geophysicists, students, data scientists and engineers in geophysics with the tools necessary to understand and apply mathematical techniques and inverse theory. MATLAB and Python scripts are used throughout the book as a means of communicating how the formulas of inverse theory can be used in computer-based data processing scenarios. MATLAB is a commercial software product of The MathWorks, Inc. Python and its many add-ons, such as the NumPy linear algebra package, the SciPy scientific function package, and the Jupyter Notebook/JupyterLab environment, are freeware that offer an alternative to MATLAB and other commercial products with license fees. Both are widely used in university settings as an environment for scientific computing.