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

What this book covers

Chapter 1, Introduction to Automated Machine Learning, covers the main concepts of automated machine learning with an overview of the types of AutoML methods and its software systems.

Chapter 2, Getting Started with AutoKeras, covers everything you need in order to get started with AutoKeras and put it into practice with the help of a foundational, well explained code example.

Chapter 3, Automating the Machine Learning Pipeline with AutoKeras, explains the standard machine learning pipeline, explains how to automate such a pipeline with AutoKeras, and describes the main data preparation best practices to apply before training a model.

Chapter 4, Image Classification and Regression Using AutoKeras, focuses on the use of AutoKeras applied to images by creating more complex and powerful image recognizers, examining how they work, and seeing how to fine-tune them to improve their performance.

Chapter 5, Text Classification and Regression Using AutoKeras, focuses on the use of AutoKeras to work with text (sequences of words). This chapter also explains what recurrent neural networks are and how they work.

Chapter 6, Working with Structured Data Using AutoKeras, enables you to explore a structured dataset, transform it, and use it as a data source for specific models, as well as create your own classification and regression models to solve tasks based on structured data.

Chapter 7, Sentiment Analysis Using AutoKeras, uses a text classifier to extract sentiments from text data and applies the concepts of text classification in a practical way by implementing the sentiment predictor.

Chapter 8, Topic Classification Using AutoKeras, focuses on the practical aspects of the text-based tasks learned in the previous chapters. It teaches you how to create a topic classifier with AutoKeras and then apply it to any topic or category-based dataset.

Chapter 9, Working with Multi-Modal Data and Multi-Task, covers the use of the AutoModel API to show how to handle multimodal and multitasking data.

Chapter 10, Exporting and Visualizing the Models, teaches you to export and import AutoKeras models and visualize graphically, as well as in real time, what is happening during the training of our models.