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

The intuition of hyperparameter tuning

In order to gain a practical intuition of the need for hyperparameter tuning, let's go through the following scenario in predicting the accuracy of a given neural network architecture on the MNIST dataset:

  • Scenario 1: High number of epochs and low learning rate
  • Scenario 2: Low number of epochs and high learning rate

Let us create the train and test datasets in a Google Cloud environment, as follows:

  1. Download the dataset:
mkdir data
curl -O
gzip -d mnist.pkl.gz
mv mnist.pkl data/            

The preceding code creates a new folder named data, downloads the MNIST dataset, and moves it into the data folder.

  1. Open Python in Terminal and import the required packages:
from __future__ import print_function 
import tensorflow as tf
import pickle # for handling the new data source
import numpy...