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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 FREE CHAPTER 2. High-Level Libraries for TensorFlow 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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Simple linear regression

You might have used other machine learning libraries; now let's practice learning the simple linear regression model using TensorFlow. We will explain the concepts first using a generated dataset before moving on to domain-specific examples.

We will use generated datasets so that readers from all different domains can learn without getting overwhelmed with the details of the specific domain of the example.

You can follow along with the code in the Jupyter notebook ch-04a_Regression.

Data preparation

To generate the dataset, we use the make_regression function from the datasets module of the sklearn library:

from sklearn import datasets as skds
X, y = skds.make_regression(n_samples=200,
...
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