Edité par O'Reilly Media (edition 1), 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
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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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Edité par O'Reilly Media, Inc, USA 2021-10-31, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
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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. 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.
Edité par O'Reilly Media, Inc, USA, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
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Edité par Oreilly & Associates Inc, 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 Aug 2021, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
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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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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 panierTaschenbuch. Etat : Neu. Practical Machine Learning for Computer Vision | End-To-End Machine Learning for Images | Valliappa Lakshmanan (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2021 | O'Reilly Media | EAN 9781098102364 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Edité par Oreilly & Associates Inc, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierPaperback. Etat : Brand New. 350 pages. 9.19x7.00x0.97 inches. In Stock. This item is printed on demand.
Edité par O'Reilly Media, Inc, USA, 2021
ISBN 10 : 1098102363 ISBN 13 : 9781098102364
Langue: anglais
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 763.