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Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with Tens

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Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition | Antonio Gulli, Amita Kapoor, Sujit Pal | ISBN: 9781838823412 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition 32,08 € Versandbereit in 1-2 Tagen. Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new second edition, updated for 2020 and featuring TensorFlow 2, the ...

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition Verlag: Packt Publishing ISBN: 1838823417

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Deep Learning mit TensorFlow, Keras und Einstieg, Konzepte und KI-Projekte mit Python, JavaScript und HTML5. Aktuell zu TensorFlow 2.0 | Deru, Matthieu, Ndiaye, Alassane | ISBN: 9783836274258 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

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Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition Antonio Gulli. 4,0 von 5 Sternen 9. Taschenbuch. 32,08 € TensorFlow 2.0 Quick Start Guide: Get up to speed with the newly introduced features of TensorFlow 2.0 Tony Holdroyd. 5,0 von 5 Sternen 1. Taschenbuch. 28,87 € Mastering OpenCV 4 with Python: A ...

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition von Antonio Gulli, Amita Kapoor, Sujit Pal Taschenbuch, 27. Dezember 2019 Verkaufsrang: 24058 Gewöhnlich versandfertig in 24 Stunden

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Best practical introduction to deep learning by author of Keras framework and Google researcher. First part of the book gives fundamental understanding and mathematical building blocks needed. Second part introduces different practical applications of deep learning networks: 1. Computer vision with convolutional neural networks (CNNs) 2.

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