Book Image

Hands-On Machine Learning on Google Cloud Platform

By : Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier
Book Image

Hands-On Machine Learning on Google Cloud Platform

By: Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier

Overview of this book

Google Cloud Machine Learning Engine combines the services of Google Cloud Platform with the power and flexibility of TensorFlow. With this book, you will not only learn to build and train different complexities of machine learning models at scale but also host them in the cloud to make predictions. This book is focused on making the most of the Google Machine Learning Platform for large datasets and complex problems. You will learn from scratch how to create powerful machine learning based applications for a wide variety of problems by leveraging different data services from the Google Cloud Platform. Applications include NLP, Speech to text, Reinforcement learning, Time series, recommender systems, image classification, video content inference and many other. We will implement a wide variety of deep learning use cases and also make extensive use of data related services comprising the Google Cloud Platform ecosystem such as Firebase, Storage APIs, Datalab and so forth. This will enable you to integrate Machine Learning and data processing features into your web and mobile applications. By the end of this book, you will know the main difficulties that you may encounter and get appropriate strategies to overcome these difficulties and build efficient systems.
Table of Contents (18 chapters)
8
Creating ML Applications with Firebase

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...