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Produkt zum Begriff Machine Learning:


  • Introducing Machine Learning
    Introducing Machine Learning

    Master machine learning concepts and develop real-world solutions Machine learning offers immense opportunities, and Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the foundations of artificial intelligence and the basic steps of any machine learning project. Next, they introduce Microsoft’s powerful ML.NET library, including capabilities for data processing, training, and evaluation. They present families of algorithms that can be trained to solve real-life problems, as well as deep learning techniques utilizing neural networks. The authors conclude by introducing valuable runtime services available through the Azure cloud platform and consider the long-term business vision for machine learning. ·        14-time Microsoft MVP Dino Esposito and Francesco Esposito help you ·         Explore what’s known about how humans learn and how intelligent software is built ·         Discover which problems machine learning can address ·         Understand the machine learning pipeline: the steps leading to a deliverable model ·         Use AutoML to automatically select the best pipeline for any problem and dataset ·         Master ML.NET, implement its pipeline, and apply its tasks and algorithms ·         Explore the mathematical foundations of machine learning ·         Make predictions, improve decision-making, and apply probabilistic methods ·         Group data via classification and clustering ·         Learn the fundamentals of deep learning, including neural network design ·         Leverage AI cloud services to build better real-world solutions faster     About This Book ·         For professionals who want to build machine learning applications: both developers who need data science skills and data scientists who need relevant programming skills ·         Includes examples of machine learning coding scenarios built using the ML.NET library

    Preis: 29.95 € | Versand*: 0 €
  • Privacy-Preserving Machine Learning
    Privacy-Preserving Machine Learning

    Privacy-Preserving Machine Learning is a practical guide to keeping ML data anonymous and secure. You'll learn the core principles behind different privacy preservation technologies, and how to put theory into practice for your own machine learning. Complex privacy-enhancing technologies are demystified through real world use cases forfacial recognition, cloud data storage, and more. Alongside skills for technical implementation, you'll learn about current and future machine learning privacy challenges and how to adapt technologies to your specific needs. By the time you're done, you'll be able to create machine learning systems that preserve user privacy without sacrificing data quality and model performance.Large-scale scandals such as the Facebook Cambridge Analytic a data breach have made many users wary of sharing sensitive and personal information. Demand has surged among machine learning engineers for privacy-preserving techniques that can keep users private details secure without adversely affecting the performance of models.

    Preis: 55.63 € | Versand*: 0 €
  • Managing Machine Learning Projects
    Managing Machine Learning Projects

    The go-to guide in machine learning projects from design to production. No ML skills required! In Managing Machine Learning Projects, you will learn essential machine learning project management techniques, including: Understanding an ML project's requirements Setting up the infrastructure for the project and resourcing a team Working with clients and other stakeholders Dealing with data resources and bringing them into the project for use Handling the lifecycle of models in the project Managing the application of ML algorithms Evaluating the performance of algorithms and models Making decisions about which models to adopt for delivery Taking models through development and testing Integrating models with production systems to create effective applications Steps and behaviours for managing the ethical implications of ML technology About the technology Companies of all shapes, sizes, and industries are investing in machine learning (ML). Unfortunately, around 85% of all ML projects fail. Managing machine learning projects requires adopting a different approach than you would take with standard software projects. You need to account for large and diverse data resources, evaluate and track multiple separate models, and handle the unforeseeable risk of poor performance. Never fear this book lays out the unique practices you will need to ensure your projects succeed!

    Preis: 56.7 € | Versand*: 0 €
  • Distributed Machine Learning Patterns
    Distributed Machine Learning Patterns

    Practical patterns for scaling machine learning from your laptop to a distributed cluster.In Distributed Machine Learning Patterns you will learn how to:Apply distributed systems patterns to build scalable and reliable machine learning projectsConstruct machine learning pipelines with data ingestion, distributed training, model serving, and moreAutomate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo WorkflowsMake trade offs between different patterns and approachesManage and monitor machine learning workloads at scaleScaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In Distributed Machine Learning Patterns, you'll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelinesDistributed Machine Learning Patterns teaches you how to scale machine learning models from your laptop to large distributed clusters. In it, you'll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines.about the technologyScaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure.about the bookDistributed Machine Learning Patterns is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud. Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Real-world scenarios provide clear examples of how to apply each pattern, alongside the potential trade offs for each approach. Once you've mastered these cutting edge techniques, you'll put them all into practice and finish up by building a comprehensive distributed machine learning system.

    Preis: 56.7 € | Versand*: 0 €
  • Ist ein Machine Learning Engineer ein Ingenieur?

    Ja, ein Machine Learning Engineer ist ein Ingenieur. Sie haben in der Regel einen technischen Hintergrund und arbeiten an der Entwicklung und Implementierung von Machine Learning-Modellen und -Algorithmen. Sie nutzen ihre technischen Fähigkeiten, um Daten zu analysieren, Modelle zu trainieren und Lösungen für komplexe Probleme zu entwickeln.

  • Wie beeinflusst Machine Learning die Automobilindustrie, die Gesundheitsversorgung und den Einzelhandel?

    Machine Learning hat die Automobilindustrie revolutioniert, indem es die Entwicklung autonomer Fahrzeuge ermöglicht hat, die die Sicherheit und Effizienz des Straßenverkehrs verbessern. In der Gesundheitsversorgung hat Machine Learning die Diagnose und Behandlung von Krankheiten verbessert, indem es Ärzten dabei hilft, präzisere und personalisierte Behandlungspläne zu erstellen. Im Einzelhandel hat Machine Learning dazu beigetragen, personalisierte Einkaufserlebnisse zu schaffen, indem es das Kaufverhalten der Kunden analysiert und Vorhersagen über ihre zukünftigen Bedürfnisse trifft. Insgesamt hat Machine Learning in diesen Branchen zu Effizienzsteigerungen, Kosteneinsparungen und einer besseren Kundenzufriedenheit geführt.

  • Was sind die Anwendungsmöglichkeiten von Machine Learning in verschiedenen Branchen?

    Machine Learning wird in der Gesundheitsbranche eingesetzt, um Krankheiten frühzeitig zu erkennen und personalisierte Behandlungspläne zu erstellen. In der Finanzbranche hilft Machine Learning bei der Betrugserkennung und Risikobewertung. Im Einzelhandel wird es genutzt, um das Kaufverhalten der Kunden vorherzusagen und personalisierte Empfehlungen zu geben.

  • Was sind die gängigsten Anwendungen von Machine Learning in der heutigen Technologie?

    Die gängigsten Anwendungen von Machine Learning in der heutigen Technologie sind personalisierte Empfehlungssysteme, automatisierte Spracherkennung und -übersetzung sowie Betrugserkennung in Finanztransaktionen. Machine Learning wird auch in der medizinischen Bildgebung, autonomen Fahrzeugen und der Vorhersage von Wetterphänomenen eingesetzt.

Ähnliche Suchbegriffe für Machine Learning:


  • Grundkurs Machine Learning (Wilmott, Paul)
    Grundkurs Machine Learning (Wilmott, Paul)

    Grundkurs Machine Learning , Maschinelles Lernen - alle Grundlagen! Paul Wilmott ist für seine erhellende und unterhaltsame Darstellung angewandter Mathematik bekannt. Von der linearen Regression bis zu Neuronalen Netzwerken führt er Sie durch alle Verfahren, und zwar komplett Software-unabhängig. Der Vorteil dabei: Jeder Schritt ist schwarz auf weiß zu sehen, kein Framework kann etwas "verstecken", es geht immer um die Sache selbst. Mit vielen Beispielen, Grafiken und Schritt-für-Schritt-Kästen. Für alle, die wirklich verstehen wollen, wie Maschinen lernen. Aus dem Inhalt: Lineare Regression k-Nearest Neighbors Naive Bayes-Klassifikatoren k-Means-Algorithmus Support Vector Machines Logistische Regression Selbstorganisierende Karten Entscheidungsbäume Reinforcement Learning Neuronale Netze , Studium & Erwachsenenbildung > Fachbücher, Lernen & Nachschlagen , Erscheinungsjahr: 20200724, Produktform: Kartoniert, Titel der Reihe: Rheinwerk Computing##, Autoren: Wilmott, Paul, Seitenzahl/Blattzahl: 256, Themenüberschrift: COMPUTERS / General, Keyword: Hand-Buch Bücher lernen Studium Grundlagen Kurse Workshops Tutorials Wissen Anleitung Training Ausbildung; Robotik; Informatik verstehen; Programmierung programmieren; KI-Welt AI; Künstliche Intelligenz; Software-Entwicklung; Coder Coden; Neuronale Netze; Deep Learning; Mathematik, Fachschema: Informatik~Programmiersprachen~EDV / Theorie / Allgemeines, Fachkategorie: Informatik~Informationstechnik (IT), allgemeine Themen, Sprache: Deutsch, Fachkategorie: Programmier- und Skriptsprachen, allgemein, Thema: Verstehen, Text Sprache: ger, Verlag: Rheinwerk Verlag GmbH, Verlag: Rheinwerk Verlag GmbH, Breite: 175, Höhe: 17, Gewicht: 480, Produktform: Klappenbroschur, Genre: Mathematik/Naturwissenschaften/Technik/Medizin, Genre: Mathematik/Naturwissenschaften/Technik/Medizin, Herkunftsland: DEUTSCHLAND (DE), Katalog: deutschsprachige Titel, Katalog: Gesamtkatalog, Katalog: Lagerartikel, Book on Demand, ausgew. Medienartikel, Relevanz: 0006, Tendenz: -1, Unterkatalog: AK, Unterkatalog: Bücher, Unterkatalog: Hardcover, Unterkatalog: Lagerartikel, WolkenId: 2327150

    Preis: 29.90 € | Versand*: 0 €
  • Automated Machine Learning in Action
    Automated Machine Learning in Action

    Optimize every stage of your machine learning pipelines with powerful automation components and cutting-edge tools like AutoKeras and KerasTuner. Automated Machine Learning in Action, filled with hands-onexamples and written in an accessible style, reveals how premade machine learning components can automate time-consuming ML tasks.Automated Machine Learning in Action teaches you to automate selecting the best machine learning models or data preparation methods for your own machine learning tasks, so your pipelines tune themselves without needing constant input. You'll quickly run through machine learning basics thatopen upon AutoML to non-data scientists, before putting AutoML into practicefor image classification, supervised learning, and more.Automated machine learning (AutoML) automates complex andtime-consuming stages in a machine learning pipeline with pre packaged optimal solutions. This frees up data scientists from data processing and manualtuning, and lets domain experts easily apply machine learning models to their projects.

    Preis: 47.07 € | Versand*: 0 €
  • Machine Learning Engineering in Action
    Machine Learning Engineering in Action

    Machine Learning Engineering in Action lays out an approach to building deployable, maintainable production machine learning systems. You will adopt software development standards that deliver better code management, and make it easier to test, scale, and even reuse your machine learning code!You will learn how to plan and scope your project, manage cross-team logistics that avoid fatal communication failures, and design your code's architecture for improved resilience. You will even discover when not to use machine learningand the alternative approaches that might be cheaper and more effective. When you're done working through this toolbox guide, you will be able to reliably deliver cost-effective solutions for organizations big and small alike.Following established processes and methodology maximizes the likelihood that your machine learning projects will survive and succeed for the long haul. By adopting standard, reproducible practices, your projects will be maintainable over time and easy for new team members to understand and adapt.

    Preis: 55.63 € | Versand*: 0 €
  • Ensemble Methods for Machine Learning
    Ensemble Methods for Machine Learning

    Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results.Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models.About the TechnologyEnsemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.

    Preis: 56.7 € | Versand*: 0 €
  • Was sind die Vorteile von Machine Learning für die Analyse großer Datenmengen?

    Machine Learning ermöglicht eine schnellere und präzisere Analyse großer Datenmengen, da Algorithmen Muster und Zusammenhänge erkennen können, die für Menschen schwer zu erkennen sind. Zudem können durch Machine Learning automatisierte Entscheidungen getroffen werden, die auf den analysierten Daten basieren. Dadurch können Unternehmen effizienter arbeiten, Kosten senken und neue Erkenntnisse gewinnen.

  • Was sind die möglichen Anwendungen von Machine Learning in der heutigen Technologie?

    Machine Learning wird in der heutigen Technologie für personalisierte Empfehlungen, Betrugserkennung und autonomes Fahren eingesetzt. Es kann auch für die medizinische Diagnose, Spracherkennung und Bilderkennung verwendet werden. Machine Learning wird auch in der Finanzbranche für Risikomanagement und automatisierte Handelsstrategien eingesetzt.

  • Was sind die wichtigsten Anwendungsgebiete von Machine Learning in der heutigen Technologie?

    Die wichtigsten Anwendungsgebiete von Machine Learning sind in der Bild- und Spracherkennung, der personalisierten Werbung und Empfehlungssystemen sowie in der medizinischen Diagnose und Prognose. Machine Learning wird auch in der Finanzbranche für Risikomanagement und Betrugserkennung eingesetzt. In der Industrie wird Machine Learning zur Optimierung von Produktionsprozessen und zur Vorhersage von Wartungsbedarf verwendet.

  • Was sind die häufigsten Anwendungen von Machine Learning in der heutigen Technologiebranche?

    Die häufigsten Anwendungen von Machine Learning in der Technologiebranche sind personalisierte Empfehlungssysteme, Betrugserkennung in Finanzdienstleistungen und Gesichtserkennung in Sicherheitssystemen. Machine Learning wird auch für Spracherkennung in virtuellen Assistenten wie Siri und Alexa sowie für automatisierte Datenanalysen in verschiedenen Branchen eingesetzt. Es wird auch für die Optimierung von Suchmaschinen, medizinische Diagnosen und autonome Fahrzeuge verwendet.

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