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Automated Machine Learning with AutoKeras

Automated Machine Learning with AutoKeras

By : Sobrecueva
4.6 (7)
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Automated Machine Learning with AutoKeras

Automated Machine Learning with AutoKeras

4.6 (7)
By: 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)
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1
Section 1: AutoML Fundamentals
5
Section 2: AutoKeras in Practice
11
Section 3: Advanced AutoKeras

Understanding topic classification

We saw a small example of topic classification in Chapter 5, Text Classification and Regression Using AutoKeras, with the example of the spam classifier. In that case, we predicted a category (spam/no spam) from the content of an email. In this section, we will use a similar text classifier to categorize each article in its corresponding topic. By doing this, we will obtain a model that determines which topics (categories) correspond to each news item.

For example, let's say our model has input the following title:

"The match could not be played due to the eruption of a tornado"

This will output the weather and sports topics, as shown in the following diagram:

Figure 8.1 – Workflow of a news topic classifier

Figure 8.1 – Workflow of a news topic classifier

The previous diagram shows a simplified version of a topic classifier pipeline. The raw text is processed by the classifier and the output will be one or more categories.

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Automated Machine Learning with AutoKeras
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