Create better and easy-to-use deep learning models with AutoKeras
Design and implement your own custom machine learning models using the features of AutoKeras
Learn how to use AutoKeras for techniques such as classification, regression, and sentiment analysis
Get familiar with advanced concepts as multi-modal, multi-task, and search space customization
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.
What you will learn
Set up a deep learning workstation with TensorFlow and AutoKeras
Automate a machine learning pipeline with AutoKeras
Create and implement image and text classifiers and regressors using AutoKeras
Use AutoKeras to perform sentiment analysis of a text, classifying it as negative or positive
Leverage AutoKeras to classify documents by topics
Make the most of AutoKeras by using its most powerful extensions
Who this book is for
This book is for machine learning and deep learning enthusiasts who want to apply automated ML techniques to their projects. Prior basic knowledge of Python programming and machine learning is expected to get the most out of this book.
Table of Contents
Introduction to Automated Machine Learning
Getting Started with AutoKeras
Automating the Machine Learning Pipeline with AutoKeras
Image Classification and Regression Using AutoKeras
Text Classification and Regression Using AutoKeras
Working with Structured Data Using AutoKeras
Sentiment Analysis Using AutoKeras
Topic Classification Using AutoKeras
Working with Multi-Modal Data and Multi-Task
Exporting and Visualizing the Models