Book Image

Machine Learning with BigQuery ML

By : Alessandro Marrandino
Book Image

Machine Learning with BigQuery ML

By: Alessandro Marrandino

Overview of this book

BigQuery ML enables you to easily build machine learning (ML) models with SQL without much coding. This book will help you to accelerate the development and deployment of ML models with BigQuery ML. The book starts with a quick overview of Google Cloud and BigQuery architecture. You'll then learn how to configure a Google Cloud project, understand the architectural components and capabilities of BigQuery, and find out how to build ML models with BigQuery ML. The book teaches you how to use ML using SQL on BigQuery. You'll analyze the key phases of a ML model's lifecycle and get to grips with the SQL statements used to train, evaluate, test, and use a model. As you advance, you'll build a series of use cases by applying different ML techniques such as linear regression, binary and multiclass logistic regression, k-means, ARIMA time series, deep neural networks, and XGBoost using practical use cases. Moving on, you'll cover matrix factorization and deep neural networks using BigQuery ML's capabilities. Finally, you'll explore the integration of BigQuery ML with other Google Cloud Platform components such as AI Platform Notebooks and TensorFlow along with discovering best practices and tips and tricks for hyperparameter tuning and performance enhancement. By the end of this BigQuery book, you'll be able to build and evaluate your own ML models with BigQuery ML.
Table of Contents (20 chapters)
Section 1: Introduction and Environment Setup
Section 2: Deep Learning Networks
Section 3: Advanced Models with BigQuery ML
Section 4: Further Extending Your ML Capabilities with GCP

Chapter 11: Implementing Deep Neural Networks

Deep Neural Networks (DNNs) are one of the most advanced techniques to implement machine learning algorithms. They're widely used for different use cases and can be considered pervasive in everyday life.

When we interact with a virtual assistant, or we use mobile applications for automatic translation and image recognition, we're leveraging the capabilities of DNNs trained with large datasets of audio and images.

After reading this chapter, you'll be able to develop, evaluate, and test a DNN using BigQuery ML. In this chapter, we'll see all the stages necessary to implement a DNN by using BigQuery ML to predict the duration of rentals related to the New York City bike-sharing service.

Using BigQuery ML, we'll go through the following topics:

  • Introducing the business scenario
  • Discovering DNNs
  • Preparing the dataset
  • Training the DNN models
  • Evaluating the DNN models
  • Using the DNN...