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

Evaluating Results with TensorBoard

In the previous chapter, we understood how a neural network works, what the various hyper parameters in a neural network are, and how they can be tweaked further to improve our model's accuracy.

Google offers TensorBoard, a visualization of the model training logs. In this chapter, we show how to use TensorBoard for TensorFlow and Keras. We interpret the visualizations generated by TensorBoard to understand the performance of our models, and also understand the other functionalities in TensorBoard that can help visualize our dataset better.

As discussed in the previous chapter, Keras as a framework is a wrapper on top of either TensorFlow or Theano. The computations that you'll use TensorFlow for, such as training a massive deep neural network, can be complex and confusing. To make it easier to understand, debug, and optimize TensorFlow...