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

Product type Paperback

Published in Apr 2018

Publisher Packt

ISBN-13 9781788393485

Length 500 pages

Edition 1st Edition

Tools

Google Cloud Platform

Concepts

Machine Learning

Authors (3):

Alexis Perrier

V Kishore Ayyadevara

Giuseppe Ciaburro

View More author details

Table of Contents (18) Chapters

Preface

1. Introducing the Google Cloud Platform FREE CHAPTER

2. Google Compute Engine

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

The GCE is a GCP core service, offering a wide variety of scalable VMs based on different OSes. The multiplicity of available OSes, the range of machines with CPUs, GPUs, small to huge disk space, and RAM make the GCE a powerful Cloud environment adapted to a wide variety of projects and contexts.

In this chapter you learned:

How to create, launch, back up, modify, and access multiple VMs
The different parameters and variables related to a VM
How to access and use the Google Shell
How to use the gcloud CLI to carry out the same operations in the GCP
How to install a data science Python stack
How to launch a Jupyter Notebook

There are many more possibilities offered by the power and flexibility of the GCE that we haven't covered. Hopefully, by the end of this chapter, you should feel comfortable working with instances that are appropriate for your projects in a data...

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Authors (3)

Perrier

Alexis Perrier is a data science consultant with experience in signal processing and stochastic algorithms. He holds a master's in mathematics from Universit Pierre et Marie Curie Paris VI and a PhD in signal processing from Tlcom ParisTech. He is actively involved in the DC data science community. He is also an avid book lover and proud owner of a real chalk blackboard, where he regularly shares his fascination of mathematical equations with his kids.

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V Kishore Ayyadevara

Kishore Ayyadevara is an entrepreneur and a hands-on leader working at the intersection of technology, data, and AI to identify and solve business problems. With over a decade of experience in leadership roles, Kishore has established and grown successful applied data science teams at American Express and Amazon, as well as a top health insurance company. In his current role, he is building a start-up focused on making AI more accessible to healthcare organizations. Outside of work, Kishore has shared his knowledge through his five books on ML/AI, is an inventor with 12 patents, and has been a speaker at multiple AI conferences.

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Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).

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