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Produkte zum Begriff Machine Learning ML:


  • gashapon machine play house candy game machine learning machine candy learning play house learning
    gashapon machine play house candy game machine learning machine candy learning play house learning

    gashapon machine play house candy game machine learning machine candy learning play house learning

    Preis: 78.25 € | Versand*: 0 €
  • gashapon machine play house candy game machine learning machine candy learning play house learning
    gashapon machine play house candy game machine learning machine candy learning play house learning

    gashapon machine play house candy game machine learning machine candy learning play house learning

    Preis: 78.25 € | Versand*: 0 €
  • gashapon machine play house candy game machine learning machine candy learning play house learning
    gashapon machine play house candy game machine learning machine candy learning play house learning

    gashapon machine play house candy game machine learning machine candy learning play house learning

    Preis: 73.07 € | Versand*: 0 €
  • gashapon machine play house candy game machine learning machine candy learning play house learning
    gashapon machine play house candy game machine learning machine candy learning play house learning

    gashapon machine play house candy game machine learning machine candy learning play house learning

    Preis: 73.38 € | Versand*: 0 €
  • Warum Deep Learning im Vergleich zu Machine Learning?

    Deep Learning unterscheidet sich von Machine Learning durch seine Fähigkeit, automatisch Merkmale aus den Daten zu extrahieren, anstatt dass diese manuell definiert werden müssen. Dadurch ist Deep Learning in der Lage, komplexere und abstraktere Muster in den Daten zu erkennen und zu lernen. Dies ermöglicht es Deep Learning-Modellen, in vielen Anwendungsbereichen, wie Bild- und Spracherkennung, bessere Leistungen zu erzielen als herkömmliche Machine Learning-Modelle.

  • Was ist Python Machine Learning?

    Python Machine Learning bezieht sich auf die Verwendung von Python-Programmierung, um maschinelles Lernen zu implementieren. Dabei werden Algorithmen und Modelle erstellt, die es Computern ermöglichen, aus Daten zu lernen und Vorhersagen zu treffen. Python bietet eine Vielzahl von Bibliotheken wie Scikit-learn, TensorFlow und Keras, die das Entwickeln von Machine-Learning-Anwendungen erleichtern. Mit Python Machine Learning können komplexe Probleme gelöst und Muster in großen Datenmengen entdeckt werden.

  • Ist Machine Learning bereits künstliche Intelligenz?

    Machine Learning ist ein Teilgebiet der künstlichen Intelligenz. Es befasst sich mit der Entwicklung von Algorithmen und Modellen, die es Computern ermöglichen, aus Daten zu lernen und Vorhersagen zu treffen. Künstliche Intelligenz umfasst jedoch auch andere Bereiche wie Expertensysteme, natürliche Sprachverarbeitung und Robotik.

  • Wie beeinflusst Machine Learning die Entwicklung von künstlicher Intelligenz?

    Machine Learning ist ein Teilgebiet der künstlichen Intelligenz, das es Computern ermöglicht, aus Daten zu lernen und Muster zu erkennen. Durch Machine Learning können Algorithmen verbessert und optimiert werden, um intelligenter zu werden. Somit trägt Machine Learning maßgeblich zur Weiterentwicklung und Verbesserung von künstlicher Intelligenz bei.

Ähnliche Suchbegriffe für Machine Learning ML:


  • 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 €
  • 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 €
  • 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 €
  • 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 €
  • Was ist der Unterschied zwischen Deep Learning und Machine Learning?

    Deep Learning ist eine spezielle Methode des Machine Learning, die auf künstlichen neuronalen Netzwerken basiert. Es ermöglicht das Lernen von hierarchischen und komplexen Merkmalsdarstellungen, um automatisch Muster und Strukturen in Daten zu erkennen. Im Gegensatz dazu ist Machine Learning ein breiterer Begriff, der verschiedene Algorithmen und Techniken umfasst, um Computermodelle zu erstellen, die aus Daten lernen und Vorhersagen treffen können. Deep Learning ist also eine Teilmenge des Machine Learning.

  • Wie beeinflusst Machine Learning die zukünftige Entwicklung von künstlicher Intelligenz?

    Machine Learning ermöglicht es künstlicher Intelligenz, aus Daten zu lernen und sich selbst zu verbessern. Durch kontinuierliches Training kann die KI immer komplexere Aufgaben bewältigen. Dadurch wird die zukünftige Entwicklung von künstlicher Intelligenz beschleunigt und ermöglicht neue Anwendungen in verschiedenen Bereichen.

  • 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.

  • Ist AWS der Standard im Machine Learning?

    AWS ist einer der führenden Anbieter von Cloud-Computing-Diensten, einschließlich Machine Learning. Es bietet eine breite Palette von ML-Diensten und Tools wie Amazon SageMaker und Amazon Rekognition, die von vielen Unternehmen genutzt werden. Obwohl AWS als Standard angesehen werden kann, gibt es auch andere Anbieter wie Google Cloud und Microsoft Azure, die ebenfalls starke ML-Funktionen bieten. Die Wahl des richtigen Anbieters hängt von den spezifischen Anforderungen und Präferenzen des Unternehmens ab.

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