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Titre : Hands-On Design Patterns and Best Practices with Julia Type de document : e-book Auteurs : Tom KWONG Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781838648817 Note générale : copyrighted Langues : Anglais (eng) Résumé : Design and develop high-performance, reusable, and maintainable applications using traditional and modern Julia patterns with this comprehensive guide Key Features Explore useful design patterns along with object-oriented programming in Julia 1.0 Implement macros and metaprogramming techniques to make your code faster, concise, and efficient Develop the skills necessary to implement design patterns for creating robust and maintainable applications Book Description Design patterns are fundamental techniques for developing reusable and maintainable code. They provide a set of proven solutions that allow developers to solve problems in software development quickly. This book will demonstrate how to leverage design patterns with real-world applications. Starting with an overview of design patterns and best practices in application design, you'll learn about some of the most fundamental Julia features such as modules, data types, functions/interfaces, and metaprogramming. You'll then get to grips with the modern Julia design patterns for building large-scale applications with a focus on performance, reusability, robustness, and maintainability. The book also covers anti-patterns and how to avoid common mistakes and pitfalls in development. You'll see how traditional object-oriented patterns can be implemented differently and more effectively in Julia. Finally, you'll explore various use cases and examples, such as how expert Julia developers use design patterns in their open source packages. By the end of this Julia programming book, you'll have learned methods to improve software design, extensibility, and reusability, and be able to use design patterns efficiently to overcome common challenges in software development. What you will learn Master the Julia language features that are key to developing large-scale software applications Discover design patterns to improve overall application architecture and design Develop reusable programs that are modular, extendable, performant, and easy to maintain Weigh up the pros and cons of using different design patterns for use cases Explore methods for transitioning from object-oriented programming to using equivalent or more advanced Julia techniques Who this book is for This book is for beginner to intermediate-level Julia programmers who want to enhance their skills in designing and developing large-scale applications. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88880110 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=501750
Titre : Hands-On Edge Analytics with Azure IoT Type de document : e-book Auteurs : Colin DOW Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781838829902 Note générale : copyrighted Langues : Anglais (eng) Résumé : Design, secure, and protect the privacy of edge analytics applications using platforms and tools such as Microsoft's Azure IoT Edge, MicroPython, and Open Source Computer Vision (OpenCV) Key Features Become well-versed with best practices for implementing automated analytical computations Discover real-world examples to extend cloud intelligence Develop your skills by understanding edge analytics and applying it to research activities Book Description Edge analytics has gained attention as the IoT model for connected devices rises in popularity. This guide will give you insights into edge analytics as a data analysis model, and help you understand why it's gaining momentum. You'll begin with the key concepts and components used in an edge analytics app. Moving ahead, you'll delve into communication protocols to understand how sensors send their data to computers or microcontrollers. Next, the book will demonstrate how to design modern edge analytics apps that take advantage of the processing power of modern single-board computers and microcontrollers. Later, you'll explore Microsoft Azure IoT Edge, MicroPython, and the OpenCV visual recognition library. As you progress, you'll cover techniques for processing AI functionalities from the server side to the sensory side of IoT. You'll even get hands-on with designing a smart doorbell system using the technologies you've learned. To remove vulnerabilities in the overall edge analytics architecture, you'll discover ways to overcome security and privacy challenges. Finally, you'll use tools to audit and perform real-time monitoring of incoming data and generate alerts for the infrastructure. By the end of this book, you'll have learned how to use edge analytics programming techniques and be able to implement automated analytical computations. What you will learn Discover the key concepts and architectures used with edge analytics Understand how to use long-distance communication protocols for edge analytics Deploy Microsoft Azure IoT Edge to a Raspberry Pi Create Node-RED dashboards with MQTT and Text to Speech (TTS) Use MicroPython for developing edge analytics apps Explore various machine learning techniques and discover how machine learning is related to edge analytics Use camera and vision recognition algorithms on the sensory side to design an edge analytics app Monitor and audit edge analytics apps Who this book is for If you are a data analyst, data architect, or data scientist who is interested in learning and practicing advanced automated analytical computations, then this book is for you. You will also find this book useful if you're looking to learn edge analytics from scratch. Basic knowledge of data analytics concepts is assumed to get the most out of this book. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88897757 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=512099
Titre : Hands-On Enterprise Automation on Linux Type de document : e-book Auteurs : James FREEMAN Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781789131611 Note générale : copyrighted Langues : Anglais (eng) Résumé : Achieve enterprise automation in your Linux environment with this comprehensive guide Key Features Automate your Linux infrastructure with the help of practical use cases and real-world scenarios Learn to plan, build, manage, and customize OS releases in your environment Enhance the scalability and efficiency of your infrastructure with advanced Linux system administration concepts Book Description Automation is paramount if you want to run Linux in your enterprise effectively. It helps you minimize costs by reducing manual operations, ensuring compliance across data centers, and accelerating deployments for your cloud infrastructures. Complete with detailed explanations, practical examples, and self-assessment questions, this book will teach you how to manage your Linux estate and leverage Ansible to achieve effective levels of automation. You'll learn important concepts on standard operating environments that lend themselves to automation, and then build on this knowledge by applying Ansible to achieve standardization throughout your Linux environments. By the end of this Linux automation book, you'll be able to build, deploy, and manage an entire estate of Linux servers with higher reliability and lower overheads than ever before. What you will learn Perform large-scale automation of Linux environments in an enterprise Overcome the common challenges and pitfalls of extensive automation Define the business processes needed to support a large-scale Linux environment Get well-versed with the most effective and reliable patch management strategies Automate a range of tasks from simple user account changes to complex security policy enforcement Learn best practices and procedures to make your Linux environment automatable Who this book is for This book is for anyone who has a Linux environment to design, implement, and maintain. Open source professionals including infrastructure architects and system administrators will find this book useful. You're expected to have experience in implementing and maintaining Linux servers along with knowledge of building, patching, and maintaining server infrastructure. Although not necessary, knowledge of Ansible or other automation technologies will be beneficial. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88880114 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=501754
Titre : Hands-On Explainable AI (XAI) with Python Type de document : e-book Auteurs : Denis ROTHMAN Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781800208131 Note générale : copyrighted Langues : Anglais (eng) Résumé : Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key Features Learn explainable AI tools and techniques to process trustworthy AI results Understand how to detect, handle, and avoid common issues with AI ethics and bias Integrate fair AI into popular apps and reporting tools to deliver business value using Python and associated tools Book Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud's XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learn Plan for XAI through the different stages of the machine learning life cycle Estimate the strengths and weaknesses of popular open-source XAI applications Examine how to detect and handle bias issues in machine learning data Review ethics considerations and tools to address common problems in machine learning data Share XAI design and visualization best practices Integrate explainable AI results using Python models Use XAI toolkits for Python in machine learning life cycles to solve business problems Who this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysis Data analysts and data scientists who want an introduction into explainable AI tools and techniques AI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88900549 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=516369
Titre : Hands-On Exploratory Data Analysis with Python Type de document : e-book Auteurs : Suresh Kumar MUKHIYA Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781789537253 Note générale : copyrighted Langues : Anglais (eng) Résumé : Discover techniques to summarize the characteristics of your data using PyPlot, NumPy, SciPy, and pandas Key Features Understand the fundamental concepts of exploratory data analysis using Python Find missing values in your data and identify the correlation between different variables Practice graphical exploratory analysis techniques using Matplotlib and the Seaborn Python package Book Description Exploratory Data Analysis (EDA) is an approach to data analysis that involves the application of diverse techniques to gain insights into a dataset. This book will help you gain practical knowledge of the main pillars of EDA - data cleaning, data preparation, data exploration, and data visualization. You'll start by performing EDA using open source datasets and perform simple to advanced analyses to turn data into meaningful insights. You'll then learn various descriptive statistical techniques to describe the basic characteristics of data and progress to performing EDA on time-series data. As you advance, you'll learn how to implement EDA techniques for model development and evaluation and build predictive models to visualize results. Using Python for data analysis, you'll work with real-world datasets, understand data, summarize its characteristics, and visualize it for business intelligence. By the end of this EDA book, you'll have developed the skills required to carry out a preliminary investigation on any dataset, yield insights into data, present your results with visual aids, and build a model that correctly predicts future outcomes. What you will learn Import, clean, and explore data to perform preliminary analysis using powerful Python packages Identify and transform erroneous data using different data wrangling techniques Explore the use of multiple regression to describe non-linear relationships Discover hypothesis testing and explore techniques of time-series analysis Understand and interpret results obtained from graphical analysis Build, train, and optimize predictive models to estimate results Perform complex EDA techniques on open source datasets Who this book is for This EDA book is for anyone interested in data analysis, especially students, statisticians, data analysts, and data scientists. The practical concepts presented in this book can be applied in various disciplines to enhance decision-making processes with data analysis and synthesis. Fundamental knowledge of Python programming and statistical concepts is all you need to get started with this book. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88884063 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=506728 PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkHands-On Machine Learning with scikit-learn and Scientific Python Toolkits / Tarek AMR / PACKT PUBLISHING (2020)
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