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

Creating a structured data classifier to predict Titanic survivors

This model will predict whether a Titanic passenger will survive the sinking of the ship based on characteristics that have been extracted from the Titanic Kaggle dataset. Although luck was an important factor in survival, some groups of people were more likely to survive than others.

There are a train dataset and a test dataset in this dataset. Both are similar datasets that include passenger information such as name, age, sex, socioeconomic class, and so on.

The train dataset (train.csv) contains details about a subset of the passengers on board (891, to be exact), revealing if they survived or not in the survived column.

The test dataset (test.csv) will be used in the final evaluation and contains similar information for the other 418 passengers.

AutoKeras will find patterns in the train data to predict whether these other 418 passengers on board (found in test.csv) survived.

The full source code notebook...