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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
Authors (3):
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
V Kishore Ayyadevara V Kishore Ayyadevara
Profile icon V Kishore Ayyadevara
Alexis Perrier Alexis Perrier
Profile icon Alexis Perrier
View More author details

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

Introducing time series

A time series constitutes a sequence of observations on a phenomenon y carried out in consecutive instants or time intervals that are usually, even if not necessarily, evenly spaced or of the same length. The trend of commodity prices, stock market indices, the BTP/BUND spread, and the unemployment rate are just a few examples of times series.

Contrary to what happens in classical statistics, where it is assumed that an independent observations come from a single random variable, in a time series, it is assumed that there are n observations coming from as many dependent random variables. The inference of the time series is thus configured as a procedure that attempts to bring the time series back to its generating process.

The time series can be of two types:

  • Deterministic: If the values of the variable can be exactly determined on the basis of the previous...
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