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 3: Automating the Machine Learning Pipeline with AutoKeras

Automating the machine learning pipeline involves automating a series of processes such as data exploration, data preprocessing, feature engineering, algorithm selection, model training, and hyperparameter tuning.

This chapter explains the standard machine learning pipeline and how to automate some of them with AutoKeras. We will also describe the main data preparation best practices to apply before training a model. The post-data preparation steps are performed by AutoKeras and we will see them in depth in later chapters.

As we saw in the first chapter, AutoKeras can automate all pipeline modeling steps by applying hyperparameter optimization and Neural Architecture Search (NAS), but some data preprocessing before these steps must be done by hand or with other tools.

We will explain the data representations expected by our model, as well as the basic preprocessing techniques that AutoKeras applies. By the...