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Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Dr. Amita Kapoor, Sujit Pal
4.3 (26)
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Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras

4.3 (26)
By: Antonio Gulli, Dr. Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
18
Index

TensorFlow Estimators

TensorFlow provides Estimators as higher-level APIs, to provide scalable and production-oriented solutions. They take care of all behind-the-scene activities such as creating computational graphs, initializing the variables, training the model, saving checkpoints, and logging TensorBoard files. TensorFlow provides two types of Estimators:

  • Canned Estimators: These are premade Estimators available in the TensorFlow estimator module. These are models in a box; you just pass them the input features and they are ready to use. Some examples are Linear Classifier, Linear Regressor, DNN Classifier, and so on.
  • Custom Estimators: Users can also create their own estimators from the models they build in TensorFlow Keras. These are user-defined Estimators.

Before being able to use TensorFlow Estimator let us understand two important components of the Estimator pipeline:

Feature columns

The feature_column module of TensorFlow 2.0 acts as a bridge between...

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Deep Learning with TensorFlow 2 and Keras
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