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

Recurrent neural network

Feedforward neural networks are based on input data that is powered to the network and converted into output. If it is a supervised learning algorithm, the output is a label that can recognize the input. Basically, these algorithms connect raw data to specific categories by recognizing patterns. Recurrent networks, on the other hand, take as input not only the current input data that is powered to the network, but also what they have experienced over time.

An recurrent neural network (RNN) is a neural model in which a bidirectional flow of information is present. In other words, while the propagation of signals in feedforward networks takes place only in a continuous manner in a direction from inputs to outputs, recurrent networks are different. In them, this propagation can also occur from a neural layer following a previous one, or between neurons belonging...