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Hands-On Machine Learning on Google Cloud Platform

You're reading from   Hands-On Machine Learning on Google Cloud Platform Implementing smart and efficient analytics using Cloud ML Engine

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788393485
Length 500 pages
Edition 1st Edition
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Authors (3):
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Alexis Perrier Alexis Perrier
Author Profile Icon Alexis Perrier
Alexis Perrier
V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (18) Chapters Close

Preface 1. Introducing the Google Cloud Platform 2. Google Compute Engine FREE CHAPTER 3. Google Cloud Storage 4. Querying Your Data with BigQuery 5. Transforming Your Data 6. Essential Machine Learning 7. Google Machine Learning APIs 8. Creating ML Applications with Firebase 9. Neural Networks with TensorFlow and Keras 10. Evaluating Results with TensorBoard 11. Optimizing the Model through Hyperparameter Tuning 12. Preventing Overfitting with Regularization 13. Beyond Feedforward Networks – CNN and RNN 14. Time Series with LSTMs 15. Reinforcement Learning 16. Generative Neural Networks 17. Chatbots

Summary

In this chapter, we learned to set up Datalab to execute neural networks on Google Cloud. We also learned the structure of a neural network and how various parameters, such as depth, number of hidden units, activation function, optimizer, batch size, and number of epochs, impact the accuracy of the model. We also saw how to implement a neural network in both Keras and TensorFlow. Topics such as using premade estimators and creating custom estimators in TensorFlow were covered.

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