Book Overview & Buying
Table Of Contents

Hands-On Machine Learning on Google Cloud Platform

By : Perrier, Giuseppe Ciaburro, V Kishore Ayyadevara

3.5 (2)

Buy this Book

Hands-On Machine Learning on Google Cloud Platform

3.5 (2)

By: Perrier, 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

Finding outliers in the data

Outliers are the values that, compared to others, are particularly extreme (a value clearly distant from the other available observations). The presence of outliers causes a hindrance because they tend to distort the results of data analysis, in particular in descriptive statistics and correlations. It is ideal to identify these outliers in the data cleaning phase itself; however, they can also be dealt with in the next step of the data analysis. Outliers can be univariate when they have an extreme value for a single variable, or multivariate when they have an unusual combination of values for a number of variables.

Outliers are the extreme values of a distribution that are characterized by being extremely high or extremely low compared to the rest of the distribution, thus representing isolated cases in respect to the rest of the distribution.

There...

Tech Concepts

Programming languages

Tech Tools