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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

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):

Perrier

Giuseppe Ciaburro

V Kishore Ayyadevara

View More author details

Table of Contents (22) Chapters

Title Page

Packt Upsell

Contributors

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

Index

Google Cloud Dataflow

Google Cloud Dataflow is a fully managed service for creating data pipelines that transform, enrich, and analyze data in batch and streaming modes. Google Cloud Dataflow extracts useful information from data, reducing operating costs without the hassle of implementing, maintaining, or resizing the data infrastructure.

A pipeline is a set of data processing elements connected in series, in which the output of one element is the input of the next. The data pipeline is implemented to increase throughput, which is the number of instructions executed in a given amount of time, parallelizing the processing flows of multiple instructions.

By appropriately defining a process management flow, significant resources can be saved in extracting knowledge from the data. Thanks to a serverless approach to provisioning and managing resources, Dataflow offers virtually unlimited capacity to solve the most serious data processing problems, but you only pay for what you use.

Google Cloud...

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Authors (3)

Perrier

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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Giuseppe Ciaburro

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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V Kishore Ayyadevara

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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