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


In this chapter, we tried to broaden the concepts underlying standard neural networks by adding features to solve more complex problems. To begin with, we discovered the architecture of CNNs. CNNs are ANNs in which the hidden layers are usually constituted by convolutional layers, pooling layers, FC layers, and normalization layers. The concepts underlying CNN were covered.

We understood training, testing, and evaluating a CNN through the analysis of a real case. For this purpose, an HWR problem was addressed in Google Cloud Platform.

Then, we explored RNN. Recurrent networks take, as their input, not only current input data that is powered to the network but also what they have experienced over time. Several RNN architectures were analyzed. In particular, we focused on LSTM networks.