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Titre : Mastering Azure Machine Learning Type de document : e-book Auteurs : Christoph KORNER Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781789807554 Note générale : copyrighted Langues : Anglais (eng) Résumé : Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes Key Features Make sense of data on the cloud by implementing advanced analytics Train and optimize advanced deep learning models efficiently on Spark using Azure Databricks Deploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS) Book Description The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud. The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure ML and takes you through the process of data experimentation, data preparation, and feature engineering using Azure ML and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure AutoML and HyperDrive, and perform distributed training on Azure ML. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure ML, along with the basics of MLOps—DevOps for ML to automate your ML process as CI/CD pipeline. By the end of this book, you'll have mastered Azure ML and be able to confidently design, build and operate scalable ML pipelines in Azure. What you will learn Setup your Azure ML workspace for data experimentation and visualization Perform ETL, data preparation, and feature extraction using Azure best practices Implement advanced feature extraction using NLP and word embeddings Train gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure ML Use hyperparameter tuning and AutoML to optimize your ML models Employ distributed ML on GPU clusters using Horovod in Azure ML Deploy, operate and manage your ML models at scale Automated your end-to-end ML process as CI/CD pipelines for MLOps Who this book is for This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88897588 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=512075
Titre : Mastering Computer Vision with TensorFlow 2.x Type de document : e-book Auteurs : Krishnendu KAR Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781838827069 Note générale : copyrighted Langues : Anglais (eng) Résumé : Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key Features Gain a fundamental understanding of advanced computer vision and neural network models in use today Cover tasks such as low-level vision, image classification, and object detection Develop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkit Book Description Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks. What you will learn Explore methods of feature extraction and image retrieval and visualize different layers of the neural network model Use TensorFlow for various visual search methods for real-world scenarios Build neural networks or adjust parameters to optimize the performance of models Understand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpainting Evaluate your model and optimize and integrate it into your application to operate at scale Get up to speed with techniques for performing manual and automated image annotation Who this book is for This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required 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/88897752 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=512094
Titre : Mastering Docker Type de document : e-book Auteurs : Russ MCKENDRICK Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781839216572 Note générale : copyrighted Langues : Anglais (eng) Résumé : Unlock the full potential of the Docker containerization platform with this practical guideKey FeaturesExplore tools such as Docker Engine, Machine, Compose, and SwarmDiscover how you can integrate Docker into your everyday workflowsGet well-versed with Kubernetes options such as Minikube, Kind, and MicroK8sBook DescriptionDocker has been a game changer when it comes to how modern applications are deployed and created. It has now grown into a key driver of innovation beyond system administration, with a significant impact on the world of web development. Mastering Docker shows you how you can ensure that you're keeping up with the innovations it's driving and be sure you're using it to its full potential. This fourth edition not only demonstrates how to use Docker more effectively but also helps you rethink and reimagine what you can achieve with it.You'll start by building, managing, and storing images along with exploring best practices for working with Docker confidently. Once you've got to grips with Docker security, the book covers essential concepts for extending and integrating Docker in new and innovative ways. You'll also learn how to take control of your containers efficiently using Docker Compose, Docker Swarm, and Kubernetes.By the end of this Docker book, you'll have a broad yet detailed sense of what's possible with Docker and how seamlessly it fits in with a range of other platforms and tools.What you will learnGet to grips with essential Docker components and conceptsDiscover the best ways to build, store, and distribute container imagesUnderstand how Docker can fit into your development workflowSecure your containers and files with Docker's security featuresExplore first-party and third-party cluster tools and pluginsLaunch and manage your Kubernetes clusters in major public cloudsWho this book is forIf you are a software architect, DevOps engineer, sysadmin, or IT professional looking to leverage Docker's extensive features for innovating any process from system administration to web development, Mastering Docker will show you how you can use it to its full potential. A basic understanding of containerization and prior Docker experience is necessary. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88906431 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=526023
Titre : Mastering KVM Virtualization Type de document : e-book Auteurs : Vedran DAKIC Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781838828714 Note générale : copyrighted Langues : Anglais (eng) Résumé : Learn how to configure, automate, orchestrate, troubleshoot, and monitor KVM-based environments capable of scaling to private and hybrid cloud modelsKey FeaturesGain expert insights into Linux virtualization and the KVM ecosystem with this comprehensive guideLearn to use various Linux tools such as QEMU, oVirt, libvirt, Cloud-Init, and Cloudbase-InitScale, monitor, and troubleshoot your VMs on various platforms, including OpenStack and AWSBook DescriptionKernel-based Virtual Machine (KVM) enables you to virtualize your data center by transforming your Linux operating system into a powerful hypervisor that allows you to manage multiple operating systems with minimal fuss. With this book, you'll gain insights into configuring, troubleshooting, and fixing bugs in KVM virtualization and related software.This second edition of Mastering KVM Virtualization is updated to cover the latest developments in the core KVM components - libvirt and QEMU. Starting with the basics of Linux virtualization, you'll explore VM lifecycle management and migration techniques. You'll then learn how to use SPICE and VNC protocols while creating VMs and discover best practices for using snapshots. As you progress, you'll integrate third-party tools with Ansible for automation and orchestration. You'll also learn to scale out and monitor your environments, and will cover oVirt, OpenStack, Eucalyptus, AWS, and ELK stack. Throughout the book, you'll find out more about tools such as Cloud-Init and Cloudbase-Init. Finally, you'll be taken through the performance tuning and troubleshooting guidelines for KVM-based virtual machines and a hypervisor.By the end of this book, you'll be well-versed with KVM virtualization and the tools and technologies needed to build and manage diverse virtualization environments.What you will learnImplement KVM virtualization using libvirt and oVirtDelve into KVM storage and networkUnderstand snapshots, templates, and live migration featuresGet to grips with managing, scaling, and optimizing the KVM ecosystemDiscover how to tune and optimize KVM virtualization hostsAdopt best practices for KVM platform troubleshootingWho this book is forIf you are a systems administrator, DevOps practitioner, or developer with Linux experience looking to sharpen your open-source virtualization skills, this virtualization book is for you. Prior understanding of the Linux command line and virtualization is required before getting started with this book. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88906434 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=526026
Titre : Mastering Machine Learning Algorithms Type de document : e-book Auteurs : Giuseppe BONACCORSO Editeur : PACKT PUBLISHING Année de publication : 2020 ISBN/ISSN/EAN : 9781838820299 Note générale : copyrighted Langues : Anglais (eng) Résumé : Updated and revised second edition of the bestselling guide to exploring and mastering the most important algorithms for solving complex machine learning problems Key Features Updated to include new algorithms and techniques Code updated to Python 3.8 & TensorFlow 2.x New coverage of regression analysis, time series analysis, deep learning models, and cutting-edge applications Book Description Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios. What you will learn Understand the characteristics of a machine learning algorithm Implement algorithms from supervised, semi-supervised, unsupervised, and RL domains Learn how regression works in time-series analysis and risk prediction Create, model, and train complex probabilistic models Cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work – train, optimize, and validate them Work with autoencoders, Hebbian networks, and GANs Who this book is for This book is for data science professionals who want to delve into complex ML algorithms to understand how various machine learning models can be built. Knowledge of Python programming is required. Nombre d'accès : Illimité En ligne : http://library.ez.neoma-bs.fr/login?url=https://www.scholarvox.com/book/88880132 Permalink : https://cataloguelibrary.neoma-bs.fr/index.php?lvl=notice_display&id=501772 PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkMicrosoft Power Platform Functional Consultant: PL-200 Exam Guide / Julian SHARP / PACKT PUBLISHING (2020)PermalinkMicrosoft SharePoint Server 2019 and SharePoint Hybrid Administration / Aaron GUILMETTE / PACKT PUBLISHING (2020)PermalinkMobile Deep Learning with TensorFlow Lite, ML Kit and Flutter / Anubhav SINGH / PACKT PUBLISHING (2020)PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPractical Threat Intelligence and Data-Driven Threat Hunting / Valentina PALACIN / PACKT PUBLISHING (2020)PermalinkPermalink
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