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 7: Sentiment Analysis Using AutoKeras

Let's start by defining the unusual term in the title. Sentiment analysis is a term that's widely used in text classification and it is basically about using natural language processing (NLP) in conjunction with machine learning (ML) to interpret and classify emotions in text.

To get an idea of this, let's imagine the task of determining whether a review for a film is positive or negative. You could do this yourself just by reading it, right? However, if our boss sends us a list of 1,000 movie reviews for tomorrow, things become complicated. That's where sentiment analysis becomes an interesting option.

In this chapter, we will use a text classifier to extract sentiments from text data. Most of the concepts of text classification were already explained in Chapter 4, Image Classification and Regression Using AutoKeras, so in this chapter, we will apply them in a practical way by implementing a sentiment predictor...