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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)

Buy this Book

Hands-On Machine Learning on Google Cloud Platform

3.5 (2)

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

Buy this Book

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

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

ML and the cloud

Introducing the GCP

Getting started with GCP

Further reading

Summary

Google Compute Engine

Setting up a data science stack on the VM

BOX the ipython console

Resources and further reading

Summary

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

Approaching big data

Data structuring

Querying the database

Google BigQuery

Visualizing data with Google Data Studio

Summary

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

Applications of machine learning

Supervised and unsupervised machine learning

Overview of machine learning techniques

Summary

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

Features of Firebase

Summary

Neural Networks with TensorFlow and Keras

Overview of a neural network

Summary

Evaluating Results with TensorBoard

Setting up TensorBoard

Overview of summary operations

Summary

Optimizing the Model through Hyperparameter Tuning

The intuition of hyperparameter tuning

Summary

Preventing Overfitting with Regularization

Intuition of over/under fitting

Summary

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

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 introduction

Reinforcement learning techniques

OpenAI Gym

Cart-Pole system

Summary

Generative Neural Networks

Unsupervised learning

Generative models

Feature extraction using RBM

Autoencoder with Keras

Magenta

Summary

Chatbots

Chatbots fundamentals

Google Cloud Dialogflow

Summary

Removing seasonality from a time series

In economic and financial analyses, which are commonly carried out on the basis of numerous indicators, the use of data presented in a seasonally adjusted form (that is, net of seasonal fluctuations), is widely used in order to be able to grasp more clearly the short-term evolution of the phenomena considered.

Seasonality, in the dynamics of a time series, is the component that repeats itself at regular intervals every year, with variations of intensity more or less similar in the same period (month, quarter, semester, and so on) of successive years; there is different intensity during the same year. Typical examples of this are a decrease in industrial production in August following holiday closures of many companies, and increase in retail sales in December due to the holiday season.

Seasonal fluctuations, disguising other movements of...

Tech Concepts

Programming languages

Tech Tools