Book Image

Hands-On Machine Learning on Google Cloud Platform

By : Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier
Book Image

Hands-On Machine Learning on Google Cloud Platform

By: Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier

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.

Related Content you might be interested in

Current Title:

Small book image
Hands-On Machine Learning on Google Cloud Platform
Giuseppe Ciaburro | V Kishore Ayyadevara | Alexis Perrier
Apr, 2018 | 500 pages
Small book image
Keras Reinforcement Learning Projects
Giuseppe Ciaburro
Sep, 2018 | 288 pages
Small book image
Hands-On Reinforcement Learning with R
Giuseppe Ciaburro
Dec, 2019 | 362 pages
Small book image
Python Machine Learning Cookbook,
Prateek Joshi | Giuseppe Ciaburro
Mar, 2019 | 642 pages
Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introducing the Google Cloud Platform
Introducing the Google Cloud Platform
ML and the cloud
Introducing the GCP
Getting started with GCP
Further reading
Summary
2
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
3
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
4
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
5
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
6
Essential Machine Learning
Essential Machine Learning
Applications of machine learning
Supervised and unsupervised machine learning
Overview of machine learning techniques
Summary
7
Google Machine Learning APIs
Google Machine Learning APIs
Vision API
Cloud Translation API
Natural Language API
Speech-to-text API
Video Intelligence API
Summary
8
Creating ML Applications with Firebase
Creating ML Applications with Firebase
Features of Firebase
Summary
9
Neural Networks with TensorFlow and Keras
Neural Networks with TensorFlow and Keras
Overview of a neural network
Summary
10
Evaluating Results with TensorBoard
Evaluating Results with TensorBoard
Setting up TensorBoard
Overview of summary operations
Summary
11
Optimizing the Model through Hyperparameter Tuning
Optimizing the Model through Hyperparameter Tuning
The intuition of hyperparameter tuning
Summary
12
Preventing Overfitting with Regularization
Preventing Overfitting with Regularization
Intuition of over/under fitting
Summary
13
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
14
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
15
Reinforcement Learning
Reinforcement Learning
Reinforcement learning introduction
Reinforcement learning techniques
OpenAI Gym
Cart-Pole system
Summary
16
Generative Neural Networks
Generative Neural Networks
Unsupervised learning
Generative models
Feature extraction using RBM
Autoencoder with Keras
Magenta
Summary
17
Chatbots
Chatbots
Chatbots fundamentals
Google Cloud Dialogflow
Summary

Vision API

The Vision API lets us build quite a few applications related to vision:

  • Detecting labels in an image
  • Detecting the text in an image
  • Face detection
  • Emotion detection
  • Logo detection
  • Landmark detection

Before we dive into building applications using the preceding, let's get a quick understanding of how they might be built, using face emotion detection as an example.

The process of detecting emotions involves:

  1. Collecting a huge set of images
  2. Hand-labeling images with the emotion that is likely represented in the image
  3. Training a convolutional neural network (CNN) (to be discussed in future chapters) to classify the emotion, based on an image as input

While the preceding steps are heavily resource intensive (as we would need a lot of humans to collect and hand-label images), there are multiple other ways to obtain face emotion detection. We are not sure how Google...

Previous Section
End of Section 2
Next Section