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)
Section 1: AutoML Fundamentals
Section 2: AutoKeras in Practice
Section 3: Advanced AutoKeras

Chapter 1: Introduction to Automated Machine Learning

In this chapter, we cover the main concepts relating to Automated Machine Learning (AutoML) with an overview of the types of AutoML methods and its software systems.

If you are a developer working with AutoML, you will be able to put your knowledge to work with this practical guide to develop and use state-of-the-art AI algorithms in your projects. By the end of this chapter, you will have a clear understanding of the anatomy of the Machine Learning (ML) workflow, what AutoML is, and its different types.

Through clear explanations of essential concepts and practical examples, you will see the differences between the standard ML and the AutoML approaches and the pros and cons of each.

In this chapter, we're going to cover the following main topics:

  • The anatomy of a standard ML workflow
  • What is AutoML?
  • Types of AutoML