Hands-On Machine Learning on Google Cloud Platform

Unleash Google's Cloud Platform to build, train and optimize machine learning models
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Hands-On Machine Learning on Google Cloud Platform

Giuseppe Ciaburro, V Kishore Ayyadevara, Alexis Perrier
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Unleash Google's Cloud Platform to build, train and optimize machine learning models
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Book Details

ISBN 139781788393485
Paperback500 pages

Book Description

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.

Table of Contents

Chapter 1: Introducing the Google Cloud Platform
ML and the cloud
Introducing the GCP
Getting started with GCP
Further reading
Summary
Chapter 2: Google Compute Engine
Google Compute Engine
Setting up a data science stack on the VM
BOX the ipython console
Resources and further reading
Summary
Chapter 3: Google Cloud Storage
Google Cloud Storage
Accessing control lists
Creating a bucket in Google Cloud Storage
Life cycle management
Google Cloud SQL
Summary
Chapter 4: Querying Your Data with BigQuery
Approaching big data
Data structuring
Querying the database
Google BigQuery
Visualizing data with Google Data Studio
Summary
Chapter 5: Transforming Your Data
How to clean and prepare the data
Finding outliers in the data
Run Job
Scale of features
Google Cloud Dataflow
Summary
Chapter 6: Essential Machine Learning
Applications of machine learning
Supervised and unsupervised machine learning
Overview of machine learning techniques
Summary
Chapter 7: Google Machine Learning APIs
Vision API
Cloud Translation API
Natural Language API
Speech-to-text API
Video Intelligence API
Summary
Chapter 8: Creating ML Applications with Firebase
Features of Firebase
Summary
Chapter 9: Neural Networks with TensorFlow and Keras
Overview of a neural network
Summary
Chapter 10: Evaluating Results with TensorBoard
Setting up TensorBoard
Overview of summary operations
Summary
Chapter 11: Optimizing the Model through Hyperparameter Tuning
The intuition of hyperparameter tuning
Summary
Chapter 12: Preventing Overfitting with Regularization
Intuition of over/under fitting
Summary
Chapter 13: 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
Chapter 14: 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
Chapter 15: Reinforcement Learning
Reinforcement learning introduction
Reinforcement learning techniques
OpenAI Gym
Cart-Pole system
Summary
Chapter 16: Generative Neural Networks
Unsupervised learning
Generative models
Feature extraction using RBM
Autoencoder with Keras
Magenta
Summary
Chapter 17: Chatbots
Chatbots fundamentals
Google Cloud Dialogflow
Summary

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

Authors

Table of Contents

Chapter 1: Introducing the Google Cloud Platform
ML and the cloud
Introducing the GCP
Getting started with GCP
Further reading
Summary
Chapter 2: Google Compute Engine
Google Compute Engine
Setting up a data science stack on the VM
BOX the ipython console
Resources and further reading
Summary
Chapter 3: Google Cloud Storage
Google Cloud Storage
Accessing control lists
Creating a bucket in Google Cloud Storage
Life cycle management
Google Cloud SQL
Summary
Chapter 4: Querying Your Data with BigQuery
Approaching big data
Data structuring
Querying the database
Google BigQuery
Visualizing data with Google Data Studio
Summary
Chapter 5: Transforming Your Data
How to clean and prepare the data
Finding outliers in the data
Run Job
Scale of features
Google Cloud Dataflow
Summary
Chapter 6: Essential Machine Learning
Applications of machine learning
Supervised and unsupervised machine learning
Overview of machine learning techniques
Summary
Chapter 7: Google Machine Learning APIs
Vision API
Cloud Translation API
Natural Language API
Speech-to-text API
Video Intelligence API
Summary
Chapter 8: Creating ML Applications with Firebase
Features of Firebase
Summary
Chapter 9: Neural Networks with TensorFlow and Keras
Overview of a neural network
Summary
Chapter 10: Evaluating Results with TensorBoard
Setting up TensorBoard
Overview of summary operations
Summary
Chapter 11: Optimizing the Model through Hyperparameter Tuning
The intuition of hyperparameter tuning
Summary
Chapter 12: Preventing Overfitting with Regularization
Intuition of over/under fitting
Summary
Chapter 13: 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
Chapter 14: 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
Chapter 15: Reinforcement Learning
Reinforcement learning introduction
Reinforcement learning techniques
OpenAI Gym
Cart-Pole system
Summary
Chapter 16: Generative Neural Networks
Unsupervised learning
Generative models
Feature extraction using RBM
Autoencoder with Keras
Magenta
Summary
Chapter 17: Chatbots
Chatbots fundamentals
Google Cloud Dialogflow
Summary

Book Details

ISBN 139781788393485
Paperback500 pages
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