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Hands-On Machine Learning on Google Cloud Platform

You're reading from  Hands-On Machine Learning on Google Cloud Platform

Product type Book
Published in Apr 2018
Publisher Packt
ISBN-13 9781788393485
Pages 500 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
LinkedIn
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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V Kishore Ayyadevara V Kishore Ayyadevara
Profile icon V Kishore Ayyadevara
LinkedIn
V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams. Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes. Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.
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Alexis Perrier Alexis Perrier
Profile icon Alexis Perrier
LinkedIn
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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Table of Contents (18) Chapters

Preface 1. Introducing the Google Cloud Platform 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

Google Compute Engine

The core service of Google Cloud Platform (GCP) is Google Compute Engine (GCE). The GCE allows you to launch spin up virtual machines (VMs) with the right operating system, size, RAM, and appropriate number of CPUs or GPUs for your needs. It is an equivalent of AWS EC2. With GCE, we dive into the core of GCP.

In this chapter, you will learn how to:

  • Create VM instances on GCE that are adapted to your projects.
  • Use Google's command-line tools to manage your VMs.
  • Set up a Python data science stack on a GCE VM with conda and scikit-learn.
  • Access your VM via a password-protected Jupyter Notebook. And we'll cover more advanced topics related to images, snapshots, pre-emptibles VMs, startup script, and IPs.

By the end of this chapter, you will be able to create and fully manage your VM both via the online console and the command-line tools, as well as...

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Authors (3)

Left arrow icon
Profile icon Giuseppe Ciaburro
LinkedIn
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).
Read more
See other products by Giuseppe Ciaburro
Profile icon V Kishore Ayyadevara
LinkedIn
V Kishore Ayyadevara leads a team focused on using AI to solve problems in the healthcare space. He has 10 years' experience in data science, solving problems to improve customer experience in leading technology companies. In his current role, he is responsible for developing a variety of cutting edge analytical solutions that have an impact at scale while building strong technical teams. Prior to this, Kishore authored three books — Pro Machine Learning Algorithms, Hands-on Machine Learning with Google Cloud Platform, and SciPy Recipes. Kishore is an active learner with keen interest in identifying problems that can be solved using data, simplifying the complexity and in transferring techniques across domains to achieve quantifiable results.
Read more
See other products by V Kishore Ayyadevara
Profile icon Alexis Perrier
LinkedIn
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.
See other products by Alexis Perrier
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