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Titre : Linux System Programming Techniques Type de document : e-book Auteurs : Jack-Benny PERSSON Editeur : PACKT PUBLISHING Année de publication : 2021 ISBN/ISSN/EAN : 9781789951288 Note générale : copyrighted Langues : Anglais (eng) Résumé : Find solutions to all your problems related to Linux system programming using practical recipes for developing your own system programsKey FeaturesDevelop a deeper understanding of how Linux system programming worksGain hands-on experience of working with different Linux projects with the help of practical examplesLearn how to develop your own programs for LinuxBook DescriptionLinux is the world's most popular open source operating system (OS). Linux System Programming Techniques will enable you to extend the Linux OS with your own system programs and communicate with other programs on the system. The book begins by exploring the Linux filesystem, its basic commands, built-in manual pages, the GNU compiler collection (GCC), and Linux system calls. You'll then discover how to handle errors in your programs and will learn to catch errors and print relevant information about them. The book takes you through multiple recipes on how to read and write files on the system, using both streams and file descriptors. As you advance, you'll delve into forking, creating zombie processes, and daemons, along with recipes on how to handle daemons using systemd. After this, you'll find out how to create shared libraries and start exploring different types of interprocess communication (IPC). In the later chapters, recipes on how to write programs using POSIX threads and how to debug your programs using the GNU debugger (GDB) and Valgrind will also be covered. By the end of this Linux book, you will be able to develop your own system programs for Linux, including daemons, tools, clients, and filters.What you will learnDiscover how to write programs for the Linux system using a wide variety of system callsDelve into the working of POSIX functionsUnderstand and use key concepts such as signals, pipes, IPC, and process managementFind out how to integrate programs with a Linux systemExplore advanced topics such as filesystem operations, creating shared libraries, and debugging your programsGain an overall understanding of how to debug your programs using ValgrindWho this book is forThis book is for anyone who wants to develop system programs for Linux and gain a deeper understanding of the Linux system. The book is beneficial for anyone who is facing issues related to a particular part of Linux system programming and is looking for specific recipes or solutions. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88914147 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=534882 LLVM Techniques, Tips, and Best Practices Clang and Middle-End Libraries / Min-Yih HSU / PACKT PUBLISHING (2021)
Titre : LLVM Techniques, Tips, and Best Practices Clang and Middle-End Libraries Type de document : e-book Auteurs : Min-Yih HSU Editeur : PACKT PUBLISHING Année de publication : 2021 ISBN/ISSN/EAN : 9781838824952 Note générale : copyrighted Langues : Anglais (eng) Résumé : Learn how you can build the next big programming language, compiler, or source code analyzer using LLVM and ClangKey FeaturesExplore Clang, LLVM's middle-end and backend, in a pragmatic wayDevelop your LLVM skillset and get to grips with a variety of common use casesEngage with real-world LLVM development through various coding examplesBook DescriptionEvery programmer or engineer, at some point in their career, works with compilers to optimize their applications. Compilers convert a high-level programming language into low-level machine-executable code. LLVM provides the infrastructure, reusable libraries, and tools needed for developers to build their own compilers. With LLVM's extensive set of tooling, you can effectively generate code for different backends as well as optimize them. In this book, you'll explore the LLVM compiler infrastructure and understand how to use it to solve different problems. You'll start by looking at the structure and design philosophy of important components of LLVM and gradually move on to using Clang libraries to build tools that help you analyze high-level source code. As you advance, the book will show you how to process LLVM IR – a powerful way to transform and optimize the source program for various purposes. Equipped with this knowledge, you'll be able to leverage LLVM and Clang to create a wide range of useful programming language tools, including compilers, interpreters, IDEs, and source code analyzers. By the end of this LLVM book, you'll have developed the skills to create powerful tools using the LLVM framework to overcome different real-world challenges.What you will learnFind out how LLVM's build system works and how to reduce the building resourceGet to grips with running custom testing with LLVM's LIT frameworkBuild different types of plugins and extensions for ClangCustomize Clang's toolchain and compiler flagsWrite LLVM passes for the new PassManagerDiscover how to inspect and modify LLVM IRUnderstand how to use LLVM's profile-guided optimizations (PGO) frameworkCreate custom compiler sanitizersWho this book is forThis book is for software engineers of all experience levels who work with LLVM. If you are an academic researcher, this book will help you learn useful LLVM skills in a short time and enable you to build your prototypes and projects quickly. Programming language enthusiasts will also find this book useful for building a new programming language with the help of LLVM. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88913200 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=534760 Machine Learning Automation with TPOT : Build, validate, and deploy fully automated machine learning models with Python / Dario RADECIC / PACKT PUBLISHING (2021)
Titre : Machine Learning Automation with TPOT : Build, validate, and deploy fully automated machine learning models with Python Type de document : e-book Auteurs : Dario RADECIC Editeur : PACKT PUBLISHING Année de publication : 2021 ISBN/ISSN/EAN : 9781800567887 Note générale : copyrighted Langues : Anglais (eng) Résumé : Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key Features Understand parallelism and how to achieve it in Python. Learn how to use neurons, layers, and activation functions and structure an artificial neural network. Tune TPOT models to ensure optimum performance on previously unseen data. Book Description The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods. With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets. By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level. What you will learn Get to grips with building automated machine learning models Build classification and regression models with impressive accuracy in a short time Develop neural network classifiers with AutoML techniques Compare AutoML models with traditional, manually developed models on the same datasets Create robust, production-ready models Evaluate automated classification models based on metrics such as accuracy, recall, precision, and f1-score Get hands-on with deployment using Flask-RESTful on localhost Who this book is for Data scientists, data analysts, and software developers who are new to machine learning and want to use it in their applications will find this book useful. This book is also for business users looking to automate business tasks with machine learning. Working knowledge of the Python programming language and beginner-level understanding of machine learning are necessary to get started. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88914148 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=581600
Titre : Machine Learning Engineering with MLflow Type de document : e-book Auteurs : Natu LAUCHANDE Editeur : PACKT PUBLISHING Année de publication : 2021 ISBN/ISSN/EAN : 9781800560796 Note générale : copyrighted Langues : Anglais (eng) Résumé : Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approachKey FeaturesExplore machine learning workflows for stating ML problems in a concise and clear manner using MLflowUse MLflow to iteratively develop a ML model and manage it Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environmentBook DescriptionMLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments.This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you'll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins.By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.What you will learnDevelop your machine learning project locally with MLflow's different featuresSet up a centralized MLflow tracking server to manage multiple MLflow experimentsCreate a model life cycle with MLflow by creating custom modelsUse feature streams to log model results with MLflowDevelop the complete training pipeline infrastructure using MLflow featuresSet up an inference-based API pipeline and batch pipeline in MLflowScale large volumes of data by integrating MLflow with high-performance big data librariesWho this book is forThis book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88919753 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=538047
Titre : Machine Learning for Time-Series with Python Type de document : e-book Auteurs : Ben AUFFARTH Editeur : PACKT PUBLISHING Année de publication : 2021 ISBN/ISSN/EAN : 9781801819626 Note générale : copyrighted Langues : Anglais (eng) Résumé : Become proficient in deriving insights from time-series data and analyzing a model's performanceKey FeaturesExplore 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 DescriptionMachine 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 learnUnderstand 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, transformers, and state-of-the-art machine learning modelsBecome familiar with many libraries like Prophet, XGboost, and TensorFlowWho this book is forThis 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. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88919726 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=538033 PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkMicrosoft Azure Security Technologies Certification and Beyond / David OKEYODE / PACKT PUBLISHING (2021)Permalink
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