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Ajouter au panierPaperback. Etat : Brand New. 295 pages. 9.00x6.00x0.70 inches. In Stock.
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Edité par Apress, Apress Nov 2020, 2020
ISBN 10 : 1484265122 ISBN 13 : 9781484265123
Langue: anglais
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Ajouter au panierTaschenbuch. Etat : Neu. Neuware -Implement deep learning applications using TensorFlow while learning the ¿why¿ through in-depth conceptual explanations.APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin 316 pp. Englisch.
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Ajouter au panierEtat : New. 1st ed. edition NO-PA16APR2015-KAP.
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Ajouter au panierTaschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Implement deep learning applications using TensorFlow while learning the 'why' through in-depth conceptual explanations. You'll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy-others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers.You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you'll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you'll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. What You'll Learn Compare competing technologies and see why TensorFlow is more popularGenerate text, image, or sound with GANsPredict the rating or preference a user will give to an itemSequence data with recurrent neural networks Who This Book Is For Data scientists and programmers new to the fields of deep learning and machine learning APIs. 316 pp. Englisch.
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EUR 59,71
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Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Implement deep learning applications using TensorFlow while learning the 'why' through in-depth conceptual explanations. You'll start by learning what deep learning offers over other machine learning models. Then familiarize yourself with several technologies used to create deep learning models. While some of these technologies are complementary, such as Pandas, Scikit-Learn, and Numpy-others are competitors, such as PyTorch, Caffe, and Theano. This book clarifies the positions of deep learning and Tensorflow among their peers.You'll then work on supervised deep learning models to gain applied experience with the technology. A single-layer of multiple perceptrons will be used to build a shallow neural network before turning it into a deep neural network. After showing the structure of the ANNs, a real-life application will be created with Tensorflow 2.0 Keras API. Next, you'll work on data augmentation and batch normalization methods. Then, the Fashion MNIST dataset will be used to train a CNN. CIFAR10 and Imagenet pre-trained models will be loaded to create already advanced CNNs. Finally, move into theoretical applications and unsupervised learning with auto-encoders and reinforcement learning with tf-agent models. With this book, you'll delve into applied deep learning practical functions and build a wealth of knowledge about how to use TensorFlow effectively. What You'll Learn Compare competing technologies and see why TensorFlow is more popularGenerate text, image, or sound with GANsPredict the rating or preference a user will give to an itemSequence data with recurrent neural networks Who This Book Is For Data scientists and programmers new to the fields of deep learning and machine learning APIs.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 86,90
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