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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
Languages
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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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Neural Networks and MLP with TensorFlow and Keras

The neural network is a modeling technique that was inspired by the structure and functioning of the brain. Just as the brain contains millions of tiny interconnected units known as neurons, the neural networks of today consist of millions of tiny interconnected computing units arranged in layers. Since the computing units of neural networks only exist in the digital world, as against the physical neurons of the brain, they are also called artificial neurons. Similarly, the neural networks (NN) are also known as the artificial neural networks (ANN).

In this chapter, we are going to further expand on the following topics:

  • The perceptron (artificial neuron)
  • Feed forward neural networks
  • MultiLayer Perceptron (MLP) for image classification
    • TensorFlow-based MLP for MNIST image classification
    • Keras-based MLP for MNIST classification...
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