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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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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

Introducing the GCP

The first cloud computing service dates back to 15 years ago, when, in July 2002, Amazon launched the AWS platform to expose technology and product data from Amazon and its affiliates, enabling developers to build innovative and entrepreneurial applications on their own. In 2006, AWS was relaunched as the EC2.

The early start of AWS gave Amazon a lead in cloud computing, one that has never faltered since. Competitors were slow to counteract and launch their own offers. The first alternative to the AWS cloud services from a major company came with the Google App Engine launched in April 2008 as a PaaS service for developing and hosting web applications. The GCP was thus born. Microsoft and IBM followed, with the Windows Azure platform launched in February 2010 and LotusLive in January 2009.

Google didn’t enter the IaaS market until much later. In 2013, Google released the Compute Engine to the general public with enterprise service-level agreements (SLA).

Mapping the GCP

With over 40 different IaaS, PaaS, and SaaS services, the GCP ecosystem is rich and complex. These services can be grouped into six different categories:

  • Hosting and computation
  • Storage and databases
  • Networking
  • ML
  • Identity and security
  • Resource management and monitoring

In the following section, we learn how to set up and manage a single VM instance on Google Compute Engine. But, before that, we need to create our account.

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