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Hands-On Machine Learning on Google Cloud Platform

Hands-On Machine Learning on Google Cloud Platform

By : Perrier, Giuseppe Ciaburro, V Kishore Ayyadevara
3.5 (2)
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Hands-On Machine Learning on Google Cloud Platform

Hands-On Machine Learning on Google Cloud Platform

3.5 (2)
By: Perrier, Giuseppe Ciaburro, V Kishore Ayyadevara

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)
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8
Creating ML Applications with Firebase

Time Series with LSTMs

In many situations involving multiple fields of real life, the need to plan future actions arises. Forecasting is an important tool for efficient planning. Moreover, this tool makes the decision-maker less susceptible to unexpected events because it requires a more scientific approach to the knowledge of the environment in which it operates. Often, the planning of future action arises from the analysis of data accumulated over time to extract information for the characterization of the phenomenon under observation.

A chronological recording of events gives rise to a new type of act, which is precisely called a time series. A time series constitutes a sequence of observations on a phenomenon carried out in consecutive instants or time intervals. Usually, even if not necessarily, they are evenly spaced or of the same length. Time series prediction requires...

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Hands-On Machine Learning on Google Cloud Platform
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