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9781098106829 - Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch di Polak, Adi (23 risultati)
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Scaling Machine Learning With Spark : Distributed Ml With Mllib, Tensorflow, and Pytorch
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Scaling Machine Learning with Spark Distributed ML with MLlib, TensorFlow, and PyTorch
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Aggiungi al carrelloPaperback. Condizione: New. Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better.Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology.You will:Explore machine learning, including distributed computing concepts and terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture.
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Scaling Machine Learning with Spark: Distributed ML with Mllib, Tensorflow, and Pytorch (Paperback or Softback)
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Paperback or Softback. Condizione: New. Scaling Machine Learning with Spark: Distributed ML with Mllib, Tensorflow, and Pytorch. Book.
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Scaling Machine Learning With Spark : Distributed Ml With Mllib, Tensorflow, and Pytorch
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Scaling Machine Learning with Spark
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Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
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Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
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Scaling Machine Learning with Spark
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Aggiungi al carrelloPaperback. Condizione: New. Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better.Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology.You will:Explore machine learning, including distributed computing concepts and terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture.
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Scaling Machine Learning With Spark : Distributed Ml With Mllib, Tensorflow, and Pytorch
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Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
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Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
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Scaling Machine Learning With Spark : Distributed Ml With Mllib, Tensorflow, and Pytorch
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Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
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Scaling Machine Learning with Spark
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ISBN 10: 1098106822 ISBN 13: 9781098106829
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Scaling Machine Learning With Spark: Distributed Ml With Mllib, Tensorflow, and Pytorch
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Scaling Machine Learning with Spark
Lingua: Inglese
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ISBN 10: 1098106822 ISBN 13: 9781098106829
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Scaling Machine Learning with Spark
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Aggiungi al carrelloPaperback. Condizione: New. Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better.Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology.You will:Explore machine learning, including distributed computing concepts and terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture.
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Aggiungi al carrelloCondizione: New. Über den AutorAdi Polak is an open source technologist who believes in communities and education, and their ability to positively impact the world around us. She is passionate about building a better world through open collaboration.
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Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
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Scaling Machine Learning with Spark
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Aggiungi al carrelloPaperback. Condizione: New. Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better.Scaling Machine Learning with Spark examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch. If you're a data scientist who works with machine learning, this book shows you when and why to use each technology.You will:Explore machine learning, including distributed computing concepts and terminologyManage the ML lifecycle with MLflowIngest data and perform basic preprocessing with SparkExplore feature engineering, and use Spark to extract featuresTrain a model with MLlib and build a pipeline to reproduce itBuild a data system to combine the power of Spark with deep learningGet a step-by-step example of working with distributed TensorFlowUse PyTorch to scale machine learning and its internal architecture.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Neuware - 'Learn how to build end-to-end scalable machine learning solutions with Apache Spark. With this practical guide, author Adi Polak introduces data and ML practitioners to creative solutions that supersede today's traditional methods. You'll learn a more holistic approach that takes you beyond specific requirements and organizational goals--allowing data and ML practitioners to collaborate and understand each other better . [Also] examines several technologies for building end-to-end distributed ML workflows based on the Apache Spark ecosystem with Spark MLlib, MLflow, TensorFlow, and PyTorch'.
