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Hands-On Machine Learning on Google Cloud Platform

By : Alexis Perrier, Bryan Fry, Antonio Gulli, Giuseppe Ciaburro, V Kishore Ayyadevara

3.5 (2)

Hands-On Machine Learning on Google Cloud Platform

3.5 (2)

By: Alexis Perrier, Bryan Fry, Antonio Gulli, 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

Speech-to-text API

The Google Cloud Speech API enables developers to convert audio to text, by applying powerful neural network models in an easy-to-use API. The API recognizes over 110 languages and variants. One can transcribe the text of users dictating to an application's microphone, enable command-and-control through voice, or transcribe audio files, among many use cases.

In order to enable the speech to text API, search for it in the console, as follows:

In the resulting web page, enable the API, as follows:

Similar to the APIs mentioned in the previous sections, credentials obtained for one API can be replicated for the other Google APIs. So, we don't have to create credentials separately for the speech to text API.

Once the API is enabled, let's start the Cloud Shell and Datalab, as we did in the previous sections.

In the following code, we transcribe...

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

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