Book Image

Automated Machine Learning with AutoKeras

By : Luis Sobrecueva
Book Image

Automated Machine Learning with AutoKeras

By: Luis Sobrecueva

Overview of this book

AutoKeras is an AutoML open-source software library that provides easy access to deep learning models. If you are looking to build deep learning model architectures and perform parameter tuning automatically using AutoKeras, then this book is for you. This book teaches you how to develop and use state-of-the-art AI algorithms in your projects. It begins with a high-level introduction to automated machine learning, explaining all the concepts required to get started with this machine learning approach. You will then learn how to use AutoKeras for image and text classification and regression. As you make progress, you'll discover how to use AutoKeras to perform sentiment analysis on documents. This book will also show you how to implement a custom model for topic classification with AutoKeras. Toward the end, you will explore advanced concepts of AutoKeras such as working with multi-modal data and multi-task, customizing the model with AutoModel, and visualizing experiment results using AutoKeras Extensions. By the end of this machine learning book, you will be able to confidently use AutoKeras to design your own custom machine learning models in your company.
Table of Contents (15 chapters)
1
Section 1: AutoML Fundamentals
5
Section 2: AutoKeras in Practice
11
Section 3: Advanced AutoKeras

Chapter 6: Working with Structured Data Using AutoKeras

In this chapter, we will focus on using AutoKeras to work with structured data, also known as tabular data. We will learn how to explore this type of dataset and what techniques to apply to solve problems based on this data source.

Once you've completed this chapter, you will be able to explore a structured dataset, transform it, and use it as a data source for specific models, as well as create your own classification and regression models to solve tasks based on structured data.

Specifically, in this chapter, we will cover the following topics:

  • Understanding structured data
  • Working with structured data
  • Creating a structured data classifier to predict Titanic survivors
  • Creating a structured data regressor to predict Boston house prices