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Zustand: As New. Unread book in perfect condition.
Anbieter: GreatBookPrices, Columbia, MD, USA
Zustand: New.
Paperback. Zustand: New. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.Google engineers Valliappa Lakshmanan, Martin Goerner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.You'll learn how to:Design ML architecture for computer vision tasksSelect a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your taskCreate an end-to-end ML pipeline to train, evaluate, deploy, and explain your modelPreprocess images for data augmentation and to support learnabilityIncorporate explainability and responsible AI best practicesDeploy image models as web services or on edge devicesMonitor and manage ML models.
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Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
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Anbieter: Rarewaves.com USA, London, LONDO, Vereinigtes Königreich
EUR 77,07
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In den WarenkorbPaperback. Zustand: New. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.Google engineers Valliappa Lakshmanan, Martin Goerner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.You'll learn how to:Design ML architecture for computer vision tasksSelect a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your taskCreate an end-to-end ML pipeline to train, evaluate, deploy, and explain your modelPreprocess images for data augmentation and to support learnabilityIncorporate explainability and responsible AI best practicesDeploy image models as web services or on edge devicesMonitor and manage ML models.
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Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
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In den WarenkorbZustand: As New. Unread book in perfect condition.
Sprache: Englisch
Verlag: O'Reilly Media, Inc, USA 2021-08-31, 2021
ISBN 10: 1098102363 ISBN 13: 9781098102364
Anbieter: Chiron Media, Wallingford, Vereinigtes Königreich
EUR 62,87
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: New.
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Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
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In den WarenkorbPaperback. Zustand: Brand New. 350 pages. 9.19x7.00x0.97 inches. In Stock.
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In den WarenkorbZustand: NEW.
EUR 63,69
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In den WarenkorbPaperback. Zustand: New. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.Google engineers Valliappa Lakshmanan, Martin Goerner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.You'll learn how to:Design ML architecture for computer vision tasksSelect a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your taskCreate an end-to-end ML pipeline to train, evaluate, deploy, and explain your modelPreprocess images for data augmentation and to support learnabilityIncorporate explainability and responsible AI best practicesDeploy image models as web services or on edge devicesMonitor and manage ML models.
Anbieter: Books Puddle, New York, NY, USA
Zustand: New.
Anbieter: moluna, Greven, Deutschland
Zustand: New. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image gene.
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
EUR 72,09
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: New. This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.Google engineers Valliappa Lakshmanan, Martin Goerner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.You'll learn how to:Design ML architecture for computer vision tasksSelect a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your taskCreate an end-to-end ML pipeline to train, evaluate, deploy, and explain your modelPreprocess images for data augmentation and to support learnabilityIncorporate explainability and responsible AI best practicesDeploy image models as web services or on edge devicesMonitor and manage ML models.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 81,95
Anzahl: 2 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 350 pages. 9.19x7.00x0.97 inches. In Stock. This item is printed on demand.