Da: HPB-Red, Dallas, TX, U.S.A.
paperback. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
Condizione: good. Supports Goodwill of Silicon Valley job training programs. The cover and pages are in Good condition! Any other included accessories are also in Good condition showing use. Use can include some highlighting and writing, page and cover creases as well as other types visible wear.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 54,42
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 54,44
Quantità: Più di 20 disponibili
Aggiungi al carrelloCondizione: New.
EUR 60,54
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Aggiungi al carrelloPaperback. Condizione: 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.
Da: Mooney's bookstore, Den Helder, Paesi Bassi
EUR 52,50
Quantità: 1 disponibili
Aggiungi al carrelloCondizione: Very good.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 56,66
Quantità: 17 disponibili
Aggiungi al carrelloCondizione: New.
EUR 77,21
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: 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.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 65,52
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: New. In.
Da: Chiron Media, Wallingford, Regno Unito
EUR 62,81
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: New.
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 62,34
Quantità: 17 disponibili
Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Majestic Books, Hounslow, Regno Unito
EUR 93,94
Quantità: 3 disponibili
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Da: Revaluation Books, Exeter, Regno Unito
EUR 88,02
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 350 pages. 9.19x7.00x0.97 inches. In Stock.
EUR 56,67
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: NEW.
EUR 62,46
Quantità: Più di 20 disponibili
Aggiungi al carrelloPaperback. Condizione: 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.
Condizione: New.
Da: moluna, Greven, Germania
EUR 76,06
Quantità: 2 disponibili
Aggiungi al carrelloCondizione: 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.
EUR 72,03
Quantità: 1 disponibili
Aggiungi al carrelloPaperback. Condizione: 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.
Da: Revaluation Books, Exeter, Regno Unito
EUR 81,88
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 350 pages. 9.19x7.00x0.97 inches. In Stock. This item is printed on demand.