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 5: Text Classification and Regression Using AutoKeras

In this chapter, we will focus on the use of AutoKeras to work with text (a sequence of words).

In the previous chapter, we saw that there was a specialized type of network suitable for image processing, called a convolutional neural network (CNN). In this chapter, we will see what recurrent neural networks (RNNs) are and how they work. An RNN is a type of neural network that is very suited to working with text.

We will also use a classifier and a regressor to solve text-based tasks. By the end of the chapter, you will have learned how to use AutoKeras to solve a wide variety of problems that are text-based, such as extracting emotions from tweets, detecting spam in emails, and so on.

In this chapter, we will cover the following topics:

  • Working with text data
  • Understanding RNNs—what are these neural networks and how do they work?
  • One-dimensional CNNs (Conv1D)
  • Creating an email spam detector...