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)
Creating ML Applications with Firebase

Unsupervised learning

Unsupervised learning is a machine learning technique that, starting from a series of inputs (system experience), is able to reclassify and organize on the basis of common characteristics to try to make predictions on subsequent inputs. Unlike supervised learning, only unlabeled examples are provided to the learner during the learning process, as the classes are not known a priori but must be learned automatically.

The following diagram shows three groups labeled from raw data:

From this diagram, it is possible to notice that the system has identified three groups on the basis of a similarity, which in this case is due to proximity. In general, unsupervised learning tries to identify the internal structure of data to reproduce it.

Typical examples of these algorithms are search engines. These programs, given one or more keywords, are able to create a list...