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

Optimizing the Model through Hyperparameter Tuning

Neural networks constitute multiple parameters that can affect the ultimate accuracy in predicting an event or a label. The typical parameters include:

  • Batch size used for training
  • Number of epochs
  • Learning rate
  • Number of hidden layers
  • Number of hidden units in each hidden layer
  • The activation function applied in the hidden layer
  • The optimizer used

From the preceding list, we can see that the number of parameters that can be tweaked is very high. This makes finding the optimal combination of hyperparameters a challenge. Hyperparameter tuning as a service provided by Cloud ML Engine comes in handy in such a scenario.

In this chapter, we will go through:

  • Why hyperparameter tuning is required
  • An overview of how hyperparameter tuning works
  • Implementing hyperparameter tuning in the cloud