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Mastering TensorFlow 1.x

You're reading from   Mastering TensorFlow 1.x Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781788292061
Length 474 pages
Edition 1st Edition
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Toc

Table of Contents (21) Chapters Close

Preface 1. TensorFlow 101 2. High-Level Libraries for TensorFlow FREE CHAPTER 3. Keras 101 4. Classical Machine Learning with TensorFlow 5. Neural Networks and MLP with TensorFlow and Keras 6. RNN with TensorFlow and Keras 7. RNN for Time Series Data with TensorFlow and Keras 8. RNN for Text Data with TensorFlow and Keras 9. CNN with TensorFlow and Keras 10. Autoencoder with TensorFlow and Keras 11. TensorFlow Models in Production with TF Serving 12. Transfer Learning and Pre-Trained Models 13. Deep Reinforcement Learning 14. Generative Adversarial Networks 15. Distributed Models with TensorFlow Clusters 16. TensorFlow Models on Mobile and Embedded Platforms 17. TensorFlow and Keras in R 18. Debugging TensorFlow Models 19. Tensor Processing Units
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Keras 101

Keras is a high-level library that allows the use of TensorFlow as a backend deep learning library. TensorFlow team has included Keras in TensorFlow Core as module tf.keras. Apart from TensorFlow, Keras also supports Theano and CNTK at the time of writing this book.

The following guiding principles of Keras have made it very popular among the deep learning community:

  • Minimalism to offer a consistent and simple API
  • Modularity to allow the representation of various elements as pluggable modules
  • Extensibility to add new modules as classes and functions
  • Python-native for both code and model configuration
  • Out-of-the-box common network architectures that support CNN, RNN, or a combination of both

Throughout the remainder of this book, we shall learn how to build different kinds of deep learning and machine learning models with both the low-level TensorFlow API and the high...

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