Sign In Start Free Trial

Book Overview & Buying
Table Of Contents
Feedback & Rating

Hands-On Machine Learning on Google Cloud Platform

By : Perrier, Giuseppe Ciaburro, V Kishore Ayyadevara

3.5 (2)

Hands-On Machine Learning on Google Cloud Platform

3.5 (2)

By: Perrier, Giuseppe Ciaburro, V Kishore Ayyadevara

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.

Preface

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Free Chapter

Introducing the Google Cloud Platform

Introducing the Google Cloud Platform

ML and the cloud

Introducing the GCP

Getting started with GCP

Further reading

Summary

Google Compute Engine

Google Compute Engine

Google Compute Engine

Setting up a data science stack on the VM

BOX the ipython console

Resources and further reading

Summary

Google Cloud Storage

Google Cloud Storage

Google Cloud Storage

Accessing control lists

Creating a bucket in Google Cloud Storage

Life cycle management

Google Cloud SQL

Summary

Querying Your Data with BigQuery

Querying Your Data with BigQuery

Approaching big data

Data structuring

Querying the database

Google BigQuery

Visualizing data with Google Data Studio

Summary

Transforming Your Data

Transforming Your Data

How to clean and prepare the data

Finding outliers in the data

Run Job

Scale of features

Google Cloud Dataflow

Summary

Essential Machine Learning

Essential Machine Learning

Applications of machine learning

Supervised and unsupervised machine learning

Overview of machine learning techniques

Summary

Google Machine Learning APIs

Google Machine Learning APIs

Vision API

Cloud Translation API

Natural Language API

Speech-to-text API

Video Intelligence API

Summary

Creating ML Applications with Firebase

Creating ML Applications with Firebase

Features of Firebase

Summary

Neural Networks with TensorFlow and Keras

Neural Networks with TensorFlow and Keras

Overview of a neural network

Summary

Evaluating Results with TensorBoard

Evaluating Results with TensorBoard

Setting up TensorBoard

Overview of summary operations

Summary

Optimizing the Model through Hyperparameter Tuning

Optimizing the Model through Hyperparameter Tuning

The intuition of hyperparameter tuning

Summary

Preventing Overfitting with Regularization

Preventing Overfitting with Regularization

Intuition of over/under fitting

Summary

Beyond Feedforward Networks – CNN and RNN

Beyond Feedforward Networks – CNN and RNN

Convolutional neural networks

Handwriting Recognition using CNN and TensorFlow

Recurrent neural network

Long short-term memory networks

Handwriting Recognition using RNN and TensorFlow

Summary

Time Series with LSTMs

Time Series with LSTMs

Introducing time series

Classical approach to time series

Time series models

Removing seasonality from a time series

LSTM for time series analysis

Summary

Reinforcement Learning

Reinforcement Learning

Reinforcement learning introduction

Reinforcement learning techniques

OpenAI Gym

Cart-Pole system

Summary

Generative Neural Networks

Generative Neural Networks

Unsupervised learning

Generative models

Feature extraction using RBM

Autoencoder with Keras

Magenta

Summary

Chatbots

Chatbots

Chatbots fundamentals

Google Cloud Dialogflow

Summary

Customer Reviews

3.5 (2)

5 star

0%

4 star

50%

3 star

50%

2 star

0%

1 star

0%

Creating ML Applications with Firebase

In the previous chapter, we learned about using some of the various Google machine learning APIs to predict/classify an event. However, we performed all our work in Datalab. In a real-world scenario, we could want to integrate machine learning APIs into a web application or a mobile application that we build. Firebase comes in handy in such a scenario. Firebase is a platform allows us to build web and mobile applications without server-side programming. Firebase provides multiple features that ensure that the developer focuses on building the application, while the backend is taken care of. Some of the features that Firebase provides are:

Real-time database
File storage
Cloud functions
Hosting
Performance monitoring
Analytics
Authentication

In this chapter, we will understand more about the various features provided by Firebase. Also, to...

Visually different images

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

Tech Tools

Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

50+ new titles added per month and exclusive early access to books as they are being written.

Hands-On Machine Learning on Google Cloud Platform

Search

Your notes and bookmarks