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Lingua: Inglese
Editore: Bpb Publications 3/22/2022, 2022
ISBN 10: 9391392229 ISBN 13: 9789391392222
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Paperback or Softback. Condizione: New. Mastering TensorFlow 2.x: Implement Powerful Neural Nets across Structured, Unstructured datasets and Time Series Data. Book.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Mastering TensorFlow 2.x | Implement Powerful Neural Nets across Structured, Unstructured datasets and Time Series Data | Rajdeep Dua | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | BPB Publications | EAN 9789391392222 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mastering TensorFlow 2.x is a must to read and practice if you are interested in building various kinds of neural networks with high level TensorFlow and Keras APIs. The book begins with the basics of TensorFlow and neural network concepts, and goes into specific topics like image classification, object detection, time series forecasting and Generative Adversarial Networks.While we are practicing TensorFlow 2.6 in this book, the version of Tensorflow will change with time; however you can still use this book to witness how Tensorflow outperforms. This book includes the use of a local Jupyter notebook and the use of Google Colab in various use cases including GAN and Image classification tasks. While you explore the performance of TensorFlow, the book also covers various concepts and in-detail explanations around reinforcement learning, model optimization and time series models.TABLE OF CONTENTS1. Getting started with TensorFlow 2.x2. Machine Learning with TensorFlow 2.x3. Keras based APIs4. Convolutional Neural Networks in Tensorflow5. Text Processing with TensorFlow 2.x6. Time Series Forecasting with TensorFlow 2.x7. Distributed Training and DataInput pipelines8. Reinforcement Learning9. Model Optimization10. Generative Adversarial Networks.