Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine / Giuseppe Ciaburro, V. Kishore Ayyadevara, Alexis Perrier.

Format:

Book

Author/Creator:

Ciaburro, Giuseppe, author.

Ayyadevara, V. Kishore, author.

Perrier, Alexis, author.

Language:

English

Subjects (All):

Google (Firm).

Google.

Machine learning.

Cloud computing.

Physical Description:

1 online resource (500 pages)

Edition:

First edition

Place of Publication:

Birmingham : Packt, 2018.

System Details:

text file

Summary:

Unleash Google's Cloud Platform to build, train and optimize machine learning models About This Book Get well versed in GCP pre-existing services to build your own smart models A comprehensive guide covering aspects from data processing, analyzing to building and training ML models A practical approach to produce your trained ML models and port them to your mobile for easy access Who This Book Is For This book is for data scientists, machine learning developers and AI developers who want to learn Google Cloud Platform services to build machine learning applications. Since the interaction with the Google ML platform is mostly done via the command line, the reader is supposed to have some familiarity with the bash shell and Python scripting. Some understanding of machine learning and data science concepts will be handy What You Will Learn Use Google Cloud Platform to build data-based applications for dashboards, web, and mobile Create, train and optimize deep learning models for various data science problems on big data Learn how to leverage BigQuery to explore big datasets Use Google's pre-trained TensorFlow models for NLP, image, video and much more Create models and architectures for Time series, Reinforcement Learning, and generative models Create, evaluate, and optimize TensorFlow and Keras models for a wide range of applications In Detail 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 th...

Contents:

Cover

Title Page

Copyright and Credits

Packt Upsell

Contributors

Table of Contents

Preface

Chapter 1: Introducing the Google Cloud Platform

ML and the cloud

The nature of the cloud

Public cloud

Managed cloud versus unmanaged cloud

IaaS versus PaaS versus SaaS

Costs and pricing

ML

Introducing the GCP

Mapping the GCP

Getting started with GCP

Project-based organization

Creating your first project

Roles and permissions

Further reading

Summary

Chapter 2: Google Compute Engine

Google Compute Engine

VMs, disks, images, and snapshots

Creating a VM

Google Shell

Google Cloud Platform SDK

Gcloud

Gcloud config

Accessing your instance with gcloud

Transferring files with gcloud

Managing the VM

IPs

Setting up a data science stack on the VM

BOX the ipython console

Troubleshooting

Adding GPUs to instances

Startup scripts and stop scripts

Resources and further reading

Chapter 3: Google Cloud Storage

Google Cloud Storage

Box-storage versus drive

Accessing control lists

Access and management through the web console

gsutil

gsutil cheatsheet

Advanced gsutil

Signed URLs

Creating a bucket in Google Cloud Storage

Google Storage namespace

Naming a bucket

Naming an object

Creating a bucket

Google Cloud Storage console

Google Cloud Storage gsutil

Life cycle management

Google Cloud SQL

Databases supported

Google Cloud SQL performance and scalability

Google Cloud SQL security and architecture

Creating Google Cloud SQL instances

Chapter 4: Querying Your Data with BigQuery

Approaching big data

Data structuring

Querying the database

SQL basics

Google BigQuery

BigQuery basics

Using a graphical web UI

Visualizing data with Google Data Studio.

Creating reports in Data Studio

Chapter 5: Transforming Your Data

How to clean and prepare the data

Google Cloud Dataprep

Exploring Dataprep console

Removing empty cells

Replacing incorrect values

Mismatched values

Finding outliers in the data

Visual functionality

Statistical information

Removing outliers

Run Job

Scale of features

Min-max normalization

z score standardization

Google Cloud Dataflow

Chapter 6: Essential Machine Learning

Applications of machine learning

Financial services

Retail industry

Telecom industry

Supervised and unsupervised machine learning

Overview of machine learning techniques

Objective function in regression

Linear regression

Decision tree

Random forest

Gradient boosting

Neural network

Logistic regression

Objective function in classification

Data splitting

Measuring the accuracy of a model

Absolute error

Root mean square error

The difference between machine learning and deep learning

Applications of deep learning

Chapter 7: Google Machine Learning APIs

Vision API

Enabling the API

Opening an instance

Creating an instance using Cloud Shell

Label detection

Text detection

Logo detection

Landmark detection

Cloud Translation API

Natural Language API

Speech-to-text API

Video Intelligence API

Chapter 8: Creating ML Applications with Firebase

Features of Firebase

Building a web application

Building a mobile application

Chapter 9: Neural Networks with TensorFlow and Keras

Overview of a neural network

Setting up Google Cloud Datalab

Installing and importing the required packages

Working details of a simple neural network

Backpropagation

Implementing a simple neural network in Keras.

Understanding the various loss functions

Softmax activation

Building a more complex network in Keras

Activation functions

Optimizers

Increasing the depth of network

Impact on change in batch size

Implementing neural networks in TensorFlow

Using premade estimators

Creating custom estimators

Chapter 10: Evaluating Results with TensorBoard

Setting up TensorBoard

Overview of summary operations

Ways to debug the code

Setting up TensorBoard from TensorFlow

Summaries from custom estimator

Chapter 11: Optimizing the Model through Hyperparameter Tuning

The intuition of hyperparameter tuning

Overview of hyperparameter tuning

Hyperparameter tuning in Google Cloud

The model file

Configuration file

Setup file

The __init__ file

Chapter 12: Preventing Overfitting with Regularization

Intuition of over/under fitting

Reducing overfitting

Implementing L2 regularization

Implementing L1 regularization

Implementing dropout

Reducing underfitting

Chapter 13: Beyond Feedforward Networks - CNN and RNN

Convolutional neural networks

Convolution layer

Rectified Linear Units

Pooling layers

Fully connected layer

Structure of a CNN

TensorFlow overview

Handwriting Recognition using CNN and TensorFlow

Run Python code on Google Cloud Shell

Recurrent neural network

Fully recurrent neural networks

Recursive neural networks

Hopfield recurrent neural networks

Elman neural networks

Long short-term memory networks

Handwriting Recognition using RNN and TensorFlow

LSTM on Google Cloud Shell

Chapter 14: Time Series with LSTMs

Introducing time series

Classical approach to time series

Estimation of the trend component

Estimating the seasonality component

Time series models.

Autoregressive models

Moving average models

Autoregressive moving average model

Autoregressive integrated moving average models

Removing seasonality from a time series

Analyzing a time series dataset

Identifying a trend in a time series

Time series decomposition

Additive method

Multiplicative method

LSTM for time series analysis

Overview of the time series dataset

Data scaling

Building the model

Making predictions

Chapter 15: Reinforcement Learning

Reinforcement learning introduction

Agent-Environment interface

Markov Decision Process

Discounted cumulative reward

Exploration versus exploitation

Reinforcement learning techniques

Q-learning

Temporal difference learning

Dynamic Programming

Monte Carlo methods

Deep Q-Network

OpenAI Gym

Cart-Pole system

Learning phase

Testing phase

Chapter 16: Generative Neural Networks

Unsupervised learning

Generative models

Restricted Boltzmann machine

Boltzmann machine architecture

Boltzmann machine disadvantages

Deep Boltzmann machines

Autoencoder

Variational autoencoder

Generative adversarial network

Adversarial autoencoder

Feature extraction using RBM

Breast cancer dataset

Data preparation

Model fitting

Autoencoder with Keras

Load data

Keras model overview

Sequential model

Keras functional API

Define model architecture

Magenta

The NSynth dataset

Chapter 17: Chatbots

Chatbots fundamentals

Chatbot history

The imitation game

Eliza

Parry

Jabberwacky

Dr. Sbaitso

ALICE

SmarterChild

IBM Watson

Building a bot

Intents

Entities

Context

Chatbots

Essential requirements

The importance of the text

Word transposition

Checking a value against a pattern.

Maintaining context

Chatbots architecture

Natural language processing

Natural language understanding

Google Cloud Dialogflow

Dialogflow overview

Basics Dialogflow elements

Agents

Intent

Entity

Action

Building a chatbot with Dialogflow

Agent creation

Intent definition

Index.

Notes:

Description based on print version record.

OCLC:

1035518777