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

Convolutional neural networks

ANN is a family of models inspired from biological neural networks (the human brain) that, starting from the mechanisms regulating natural neural networks, plan to simulate human thinking. They are used to estimate or approximate functions that may depend on a large number of inputs, many of which are often unknown. ANNs are generally presented as interconnected neuron systems among which an exchange of messages takes place. Each connection has a related weight; the value of the weight is adjustable based on experience, and this makes neural networks an instrument adaptable to the various types of input and having the ability to learn.

ANNs define the neuron as a central processing unit, which performs a mathematical operation to generate one output from a set of inputs. The output of a neuron is a function of the weighted sum of the inputs plus the...