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Learn TensorFlow Enterprise

You're reading from   Learn TensorFlow Enterprise Build, manage, and scale machine learning workloads seamlessly using Google's TensorFlow Enterprise

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781800209145
Length 314 pages
Edition 1st Edition
Languages
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Author (1):
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KC Tung KC Tung
Author Profile Icon KC Tung
KC Tung
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Toc

Table of Contents (15) Chapters Close

Preface 1. Section 1 – TensorFlow Enterprise Services and Features
2. Chapter 1: Overview of TensorFlow Enterprise FREE CHAPTER 3. Chapter 2: Running TensorFlow Enterprise in Google AI Platform 4. Section 2 – Data Preprocessing and Modeling
5. Chapter 3: Data Preparation and Manipulation Techniques 6. Chapter 4: Reusable Models and Scalable Data Pipelines 7. Section 3 – Scaling and Tuning ML Works
8. Chapter 5: Training at Scale 9. Chapter 6: Hyperparameter Tuning 10. Section 4 – Model Optimization and Deployment
11. Chapter 7: Model Optimization 12. Chapter 8: Best Practices for Model Training and Performance 13. Chapter 9: Serving a TensorFlow Model 14. Other Books You May Enjoy

Summary

In this chapter, we learned how to use Keras Tuner in Google Cloud AI Platform. We learned how to run the hyperparameter search, and we learned how to train a model with the best hyperparameter configuration. We have also seen that in a typical Keras style, integrating Keras Tuner into our existing model training workflow is very easy, especially with the simple treatment of hyperparameters as just arrays of a certain data type. This really opens up the choices for hyperparameters, and we do not need to implement the search logic or complicated conditional loops to keep track of the results.

In the next chapter, we will see the latest model optimization techniques that reduce the model size. As a result, our model can be leaner and more compact.

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