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

Neural Networks with TensorFlow and Keras

Neural network is a supervised learning algorithm that is loosely inspired by the way the brain functions. Similarly to the way neurons are connected to each other in the brain, a neural network takes an input and passes it through a function, based on which certain subsequent neurons get excited, and the output is produced.

In this chapter, we will focus on the practical implementation of neural networks with TensorFlow and Keras. TensorFlow provides a low-level framework to create neural network models. Keras is a high-level neural network API that significantly simplifies the task of defining neural network models. We'll show how to use Keras on top of TensorFlow to define and train models on GCP. We'll present the Keras API in Python and work with a simple feedforward network applied on the classic MNIST dataset. Also, we...