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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 €
  • 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 €
  • 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 €

Ähnliche Suchbegriffe für Machine Learning:


  • Machine Learning with Python for Everyone
    Machine Learning with Python for Everyone

    The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with PythonMachine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning systems, even if you’re an absolute beginner. If you can write some Python code, this book is for you, no matter how little college-level math you know. Principal instructor Mark E. Fenner relies on plain-English stories, pictures, and Python examples to communicate the ideas of machine learning.Mark begins by discussing machine learning and what it can do; introducing key mathematical and computational topics in an approachable manner; and walking you through the first steps in building, training, and evaluating learning systems. Step by step, you’ll fill out the components of a practical learning system, broaden your toolbox, and explore some of the field’s most sophisticated and exciting techniques. Whether you’re a student, analyst, scientist, or hobbyist, this guide’s insights will be applicable to every learning system you ever build or use.Understand machine learning algorithms, models, and core machine learning conceptsClassify examples with classifiers, and quantify examples with regressorsRealistically assess performance of machine learning systemsUse feature engineering to smooth rough data into useful formsChain multiple components into one system and tune its performanceApply machine learning techniques to images and textConnect the core concepts to neural networks and graphical modelsLeverage the Python scikit-learn library and other powerful toolsRegister your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

    Preis: 39.58 € | Versand*: 0 €
  • Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications
    Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications

    Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings. –From the Foreword by Paul Dix, series editor Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory.   Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish.   The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments.   Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximize development efficiency in production projectsLearn from practical Python code examples and visualizations that bring essential algorithmic concepts to lifeStart with simple heuristics and improve them as your data pipeline maturesAvoid bad conclusions by implementing foundational error analysis techniquesCommunicate your results with basic data visualization techniquesMaster basic machine learning techniques, starting with linear regression and random forestsPerform classification and clustering on both vector and graph dataLearn the basics of graphical models and Bayesian inferenceUnderstand correlation and causation in machine learning modelsExplore overfitting, model capacity, and other advanced machine learning techniquesMake informed architectural decisions about storage, data transfer, computation, and communication Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

    Preis: 35.3 € | Versand*: 0 €
  • Easy Learning
    Easy Learning

    Kinder-Wanduhr "Easy Learning", Durchmesser 30 cm, geräuscharm

    Preis: 25.49 € | Versand*: 6.95 €
  • Interface Design for Learning: Design Strategies for Learning Experiences
    Interface Design for Learning: Design Strategies for Learning Experiences

    In offices, colleges, and living rooms across the globe, learners of all ages are logging into virtual laboratories, online classrooms, and 3D worlds. Kids from kindergarten to high school are honing math and literacy skills on their phones and iPads. If that weren’t enough, people worldwide are aggregating internet services (from social networks to media content) to learn from each other in “Personal Learning Environments.” Strange as it sounds, the future of education is now as much in the hands of digital designers and programmers as it is in the hands of teachers.And yet, as interface designers, how much do we really know about how people learn? How does interface design actually impact learning? And how do we design environments that support both the cognitive and emotional sides of learning experiences? The answers have been hidden away in the research on education, psychology, and human computer interaction, until now. Packed with over 100 evidence-based strategies, in this book you’ll learn how to:Design educational games, apps, and multimedia interfaces in ways that enhance learningSupport creativity, problem-solving, and collaboration through interface designDesign effective visual layouts, navigation, and multimedia for online and mobile learningImprove educational outcomes through interface design.

    Preis: 18.18 € | Versand*: 0 €

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