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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks with Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Length 320 pages
Edition 1st Edition
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Authors (4):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Fabrizio Milo Fabrizio Milo
Author Profile Icon Fabrizio Milo
Fabrizio Milo
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning 2. First Look at TensorFlow FREE CHAPTER 3. Using TensorFlow on a Feed-Forward Neural Network 4. TensorFlow on a Convolutional Neural Network 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. GPU Computing 8. Advanced TensorFlow Programming 9. Advanced Multimedia Programming with TensorFlow 10. Reinforcement Learning

TensorFlow and Keras

In this section we are going to address a very important feature for all data scientists and machine learning enthusiasts which is the integration of TensorFlow and Keras. Having this feature on board, you will be able to build a very complex deep learning systems with very few lines of code.

Figure 5: TensorFlow and Keras Integration (Source: https://blog.keras.io/img/keras-tensorflow-logo.jpg)

What is Keras?

Keras is an API that makes using and building deep learning models easier and faster. So it's a deep learning toolbox that's all about:

  • Ease of use
  • Reducing complexity
  • Reducing cognitive load

And by making deep learning easier to use what happens is that you are making it accessible to more people. So the key design concept...

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