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

Cart-Pole system

The Cart-Pole system is a classic problem of reinforced learning. The system consists of a pole (which acts like an inverted pendulum) attached to a cart via a joint, as shown in the following figure:

The system is controlled by applying a force of +1 or -1 to the cart. The force applied to the cart can be controlled, and the objective is to swing the pole upwards and stabilize it. This must be done without the cart falling to the ground. At every step, the agent can choose to move the cart left or right, and it receives a reward of 1 for every time step that the pole is balanced. If the pole ever deviates by more than 15 degrees from upright, then the procedure ends.

To run the Cart-Pole example using the OpenAI Gym library, simply type the following code:

import gym
env = gym.make('CartPole-v0')
for i in range(1000):