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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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Classical Machine Learning with TensorFlow

Machine learning is an area of computer science that involves research, development, and application of algorithms to make computing machines learn from data. The models learned by computing machines are used to make predictions and forecasts. Machine learning researchers and engineers achieve this goal by building models and then using these models for predictions. It’s common knowledge now that machine learning has been used highly successfully in various areas such as natural language understanding, video processing, image recognition, speech, and vision.

Let's talk about models. All of the machine learning problems are abstracted to the following equation in one form or another:

Here, y is the output or target and x is the input or features. If x is a collection of features, we also call it a feature vector and denote...

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