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Ajouter au panierEtat : Gut. Zustand: Gut | Seiten: 463 | Sprache: Englisch | Produktart: Bücher.
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Edité par O'Reilly Media 8/10/2021, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
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Ajouter au panierPaperback or Softback. Etat : New. Practical Machine Learning for Computer Vision: End-To-End Machine Learning for Images 1.68. Book.
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Ajouter au panierPaperback. Etat : 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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Ajouter au panierPaperback. Etat : 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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Ajouter au panierEtat : New. 2021. Paperback. . . . . .
Edité par O'Reilly Media, Inc, USA, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
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Ajouter au panierPaperback. Etat : 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.
Edité par O'reilly Media Sep 2021, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 75,78
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware - 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.
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EUR 88,76
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Ajouter au panierEtat : New. 2021. Paperback. . . . . . Books ship from the US and Ireland.
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-Uni
EUR 91,57
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Ajouter au panierPaperback. Etat : 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.
Edité par Oreilly & Associates Inc, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 88,75
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Ajouter au panierPaperback. Etat : Brand New. 350 pages. 9.19x7.00x0.97 inches. In Stock.
Edité par O'reilly Media Sep 2021, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
Vendeur : Wegmann1855, Zwiesel, Allemagne
EUR 90
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -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.
Edité par O'reilly Media Sep 2021, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 90
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -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. 463 pp. Englisch.
Edité par O'reilly Media Sep 2021, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
Vendeur : Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Allemagne
EUR 90
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -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. 463 pp. Englisch.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 93,97
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Edité par O'reilly Media Sep 2021, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -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.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 463 pp. Englisch.
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